TestingTrainedNetwork-zelda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install tensorflow==1.15\n",
    "#Install stable-baselines as described in the documentation\n",
    "\n",
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "from utils import get_exp_name, max_exp_idx, load_model, make_vec_envs\n",
    "from stable_baselines import PPO2\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "import model\n",
    "model.FullyConvPolicy = model.FullyConvPolicySmallMap\n",
    "# from model import FullyConvPolicySmallMap as FullyConvPolicy\n",
    "\n",
    "model = PPO2.load(\"my_models/sokoban/wide/model_1.pkl\")\n",
    "# model.save(\"tests/zelda_turtle_onlymap_3_test\", cloudpickle=False)\n",
    "# model = PPO2.load(\"runs/binary_narrow_no_visits_no_heatmap_no_changes_1_log/latest_model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'model' has no attribute 'predict'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_8416\\749116970.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdones\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[0mi\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 60\u001b[1;33m     \u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_states\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     61\u001b[0m     \u001b[1;31m#action = np.array([env.action_space.sample()])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m     \u001b[0mframes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_attr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pcgrl_env'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_observation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'map'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'model' has no attribute 'predict'"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def show_state(env, l, c, r, step=0, name=\"\", info=\"\"):\n",
    "    fig = plt.figure(10)\n",
    "    plt.clf()\n",
    "    \n",
    "#     plt.title(\"{} | Step: {} Path: {} Changes: {} Regions: {}\".format(name, step, l[-1], c[-1], r[-1]))\n",
    "    \n",
    "#     ax1 = fig.add_subplot(1,4,1)\n",
    "    ax1 = plt.imshow(env.render(mode='rgb_array'))\n",
    "    plt.axis('off')\n",
    "    \n",
    "#     ax2 = fig.add_subplot(1,4,2)\n",
    "#     ax2 = plt.plot(l)\n",
    "    \n",
    "#     ax3 = fig.add_subplot(1,4,3)\n",
    "#     ax3 = plt.plot(c)\n",
    "    \n",
    "#     ax4 = fig.add_subplot(1,4,4)\n",
    "#     ax4 = plt.plot(r)\n",
    "    \n",
    "    \n",
    "#     fig.set_figwidth(15)\n",
    "#     plt.tight_layout()\n",
    "    \n",
    "    display.clear_output(wait=True)\n",
    "    display.display(plt.gcf())\n",
    "\n",
    "def get_env_fun(p_name, r_name, crop_size, ch_perc):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "env = DummyVecEnv([get_env_fun('zelda', 'wide', 22, 0.4)])\n",
    "# model = PPO2(CustomPolicy, env, verbose=1, tensorboard_log=\"./runs\")\n",
    "\n",
    "# Enjoy trained agent\n",
    "path_length = []\n",
    "changes = []\n",
    "regions = []\n",
    "obs = env.reset()\n",
    "obs = env.reset()\n",
    "fixed_array = None\n",
    "if fixed_array is not None:\n",
    "    env.get_attr('pcgrl_env')[0]._rep._map = np.array(fixed_array)\n",
    "    env.get_attr('pcgrl_env')[0]._rep._old_map = np.array(fixed_array)\n",
    "frames = [env.get_attr('pcgrl_env')[0]._rep.get_observation()['map']]\n",
    "dones = False\n",
    "i = 0\n",
    "while not dones:\n",
    "    i += 1\n",
    "    action, _states = model.predict(obs)\n",
    "    #action = np.array([env.action_space.sample()])\n",
    "    frames.append(env.get_attr('pcgrl_env')[0]._rep.get_observation()['map'])\n",
    "    obs, rewards, dones, info = env.step(action)\n",
    "#     path_length.append(info[0]['path-length'])\n",
    "#     changes.append(rewards[0])\n",
    "#     regions.append(info[0]['regions'])\n",
    "    if dones:\n",
    "#         print(\"YAAAAAAAAAAAAAY\")\n",
    "        break\n",
    "    show_state(env, path_length, changes, regions, i)\n",
    "plt.show()\n",
    "print(info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Binary Model Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model model_1\n",
      "my_models/zelda/narrow/model_1.pkl\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing model_1_1 at change percentage of 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:51: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing model_1_1 at change percentage of 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:52: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:53: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing model_1_1 at change percentage of 0.2\n",
      "Testing model_1_1 at change percentage of 0.30000000000000004\n",
      "Testing model_1_1 at change percentage of 0.4\n",
      "Testing model_1_1 at change percentage of 0.5\n",
      "Testing model_1_1 at change percentage of 0.6000000000000001\n",
      "Testing model_1_1 at change percentage of 0.7000000000000001\n",
      "Testing model_1_1 at change percentage of 0.8\n",
      "Testing model_1_1 at change percentage of 0.9\n",
      "Testing model_1_1 at change percentage of 1.0\n",
      "Loading model model_1\n",
      "my_models/zelda/turtle/model_1.pkl\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing model_1_1 at change percentage of 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:51: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing model_1_1 at change percentage of 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:52: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:53: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing model_1_1 at change percentage of 0.2\n",
      "Testing model_1_1 at change percentage of 0.30000000000000004\n",
      "Testing model_1_1 at change percentage of 0.4\n",
      "Testing model_1_1 at change percentage of 0.5\n",
      "Testing model_1_1 at change percentage of 0.6000000000000001\n",
      "Testing model_1_1 at change percentage of 0.7000000000000001\n",
      "Testing model_1_1 at change percentage of 0.8\n",
      "Testing model_1_1 at change percentage of 0.9\n",
      "Testing model_1_1 at change percentage of 1.0\n",
      "Loading model model_1\n",
      "my_models/zelda/wide/model_1.pkl\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing model_1_1 at change percentage of 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:51: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing model_1_1 at change percentage of 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:52: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:53: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing model_1_1 at change percentage of 0.2\n",
      "Testing model_1_1 at change percentage of 0.30000000000000004\n",
      "Testing model_1_1 at change percentage of 0.4\n",
      "Testing model_1_1 at change percentage of 0.5\n",
      "Testing model_1_1 at change percentage of 0.6000000000000001\n",
      "Testing model_1_1 at change percentage of 0.7000000000000001\n",
      "Testing model_1_1 at change percentage of 0.8\n",
      "Testing model_1_1 at change percentage of 0.9\n",
      "Testing model_1_1 at change percentage of 1.0\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "import model\n",
    "from model import FullyConvPolicySmallMap,FullyConvPolicyBigMap\n",
    "from utils import get_exp_name, max_exp_idx, load_model, make_vec_envs\n",
    "from stable_baselines import PPO2\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import zlib\n",
    "import math\n",
    "import os\n",
    "from sklearn.decomposition import PCA\n",
    "from gym_pcgrl.envs.helper import run_dikjstra, _get_certain_tiles, get_tile_locations\n",
    "\n",
    "binary_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 48],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 48 and info['regions'] == 1]\n",
    "}\n",
    "binaryimp_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'path-imp': lambda info: info['path-imp'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathimp_const': lambda info: [0, 1][info['path-imp'] >= 20],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-imp'] >= 20 and info['regions'] == 1]\n",
    "}\n",
    "zelda_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'key': lambda info: info['key'],\n",
    "    'door': lambda info: info['door'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'nearestenemy': lambda info: info['nearest-enemy'],\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'key_const': lambda info: [0, 1][info['key'] == 1],\n",
    "    'door_const': lambda info: [0, 1][info['door'] == 1],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'nearestenemy_const': lambda info: [0, 1][info['nearest-enemy'] >= 4],\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 16],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 16 and info['nearest-enemy'] >= 4]\n",
    "}\n",
    "sokoban_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'crate': lambda info: info['crate'],\n",
    "    'target': lambda info: info['target'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'sollength': lambda info: info['sol-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'ratio_const': lambda info: [0, 1][info['crate'] == info['target'] and info['crate'] > 0],\n",
    "    'has_sol': lambda info: [0, 1][info['sol-length'] >= 1],\n",
    "    'success': lambda info: [0, 1][info['sol-length'] >= 18]\n",
    "}\n",
    "lambdas = {\n",
    "    'binary': binary_lambdas,\n",
    "    'binaryimp': binaryimp_lambdas,\n",
    "    'zelda': zelda_lambdas,\n",
    "    'sokoban': sokoban_lambdas\n",
    "}\n",
    "\n",
    "def calc_tp_count(lvl, p_size):\n",
    "    padded_lvl = np.pad(lvl, p_size, constant_values=1)\n",
    "    pattern_dict = {}\n",
    "    for (y, x), v in np.ndenumerate(lvl):\n",
    "        sy, sx = y+p_size-p_size//2, x+p_size-p_size//2\n",
    "        pattern = str(padded_lvl[sy:sy+p_size,sx:sx+p_size])\n",
    "        if not (pattern in pattern_dict):\n",
    "            pattern_dict[pattern] = 0\n",
    "        pattern_dict[pattern] += 1\n",
    "    return pattern_dict\n",
    "        \n",
    "def calc_tp_kldiv(lvl1, lvl2, p_size=3, epsilon = 1e-6):\n",
    "    p_dict = calc_tp_count(lvl1, p_size)\n",
    "    q_dict = calc_tp_count(lvl2, p_size)\n",
    "    t_patterns = set()\n",
    "    t_patterns.update(p_dict.keys())\n",
    "    t_patterns.update(q_dict.keys())\n",
    "    total_p = sum(p_dict.values())\n",
    "    total_q = sum(q_dict.values())\n",
    "    value = 0\n",
    "    for x in t_patterns:\n",
    "        p_dash = epsilon/((total_p + epsilon) * (1 + epsilon))\n",
    "        q_dash = epsilon/((total_q + epsilon) * (1 + epsilon))\n",
    "        if x in p_dict:\n",
    "            p_dash = (p_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        if x in q_dict:\n",
    "            q_dash = (q_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        value += p_dash * math.log(p_dash/q_dash)\n",
    "    return value\n",
    "\n",
    "def calc_longest_path(map):\n",
    "    map_locations = get_tile_locations(map, [0, 1])\n",
    "    empty_tiles = _get_certain_tiles(map_locations, passable_values)\n",
    "    final_visited_map = np.zeros((len(map), len(map[0])))\n",
    "    final_value = 0\n",
    "    final_loc = (0,0)\n",
    "    final_map = None\n",
    "    for (x,y) in empty_tiles:\n",
    "        if final_visited_map[y][x] > 0:\n",
    "            continue\n",
    "        dikjstra_map, visited_map = run_dikjstra(x, y, map, [0])\n",
    "        final_visited_map += visited_map\n",
    "        (my,mx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        dikjstra_map, _ = run_dikjstra(mx, my, map, passable_values)\n",
    "        (fy, fx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        max_value = np.max(dikjstra_map)\n",
    "        if max_value > final_value:\n",
    "            final_value = max_value\n",
    "            final_loc = (fx, fy)\n",
    "            final_map = dikjstra_map\n",
    "    path = [final_loc]\n",
    "    current = final_loc\n",
    "    while final_map[current[1]][current[0]] != 0:\n",
    "        small = (current[0], current[1])\n",
    "        for (dx,dy) in [(0,1), (0,-1), (1,0), (-1,0)]:\n",
    "            new = (current[0] + dx, current[1] + dy)\n",
    "            if final_map[new[1]][new[0]] == -1 or new[0] < 0 or new[1] < 0 or new[0] >= final_map.shape[1] or new[1] >= final_map.shape[0]:\n",
    "                continue\n",
    "            if final_map[new[1]][new[0]] < final_map[small[1]][small[0]]:\n",
    "                small = new\n",
    "        path.append(small)\n",
    "        current = small\n",
    "    return path\n",
    "\n",
    "def calc_hamming(lvl1, lvl2):\n",
    "    return np.clip(abs(lvl1 - lvl2), 0, 1).sum()/(lvl1.shape[0] * lvl1.shape[1])\n",
    "\n",
    "def calc_compression(lvl1, lvl2):\n",
    "    c_1 = zlib.compress(lvl1.tobytes(), level=9)\n",
    "    c_2 = zlib.compress(lvl2.tobytes(), level=9)\n",
    "    cb = zlib.compress(lvl1.tobytes() + lvl2.tobytes(), level=9)\n",
    "    return (len(cb) - min(len(c_1), len(c_2))) / max(len(c_1), len(c_2))\n",
    "\n",
    "div_func = {\n",
    "    'hamming': lambda lvl1, lvl2: calc_hamming(lvl1, lvl2),\n",
    "    'tpkldiv': lambda lvl1, lvl2: calc_tp_kldiv(lvl1, lvl2),\n",
    "    'compression': lambda lvl1, lvl2: calc_compression(lvl1, lvl2)\n",
    "}\n",
    "\n",
    "def get_pca_diversity(lvls):\n",
    "    pca2d = PCA(2)\n",
    "    temp_reshaped_lvls = np.array(lvls).reshape((len(lvls), -1))\n",
    "    projected_lvls = pca2d.fit_transform(temp_reshaped_lvls)\n",
    "    variance = np.var(projected_lvls, axis=0)\n",
    "    return [np.mean(variance)]\n",
    "\n",
    "def get_svd_diversity(lvls):\n",
    "    normalized = []\n",
    "    for i in range(len(lvls)):\n",
    "        normalized.append(lvls[i] / np.linalg.norm(lvls[i]))\n",
    "    temp_reshaped_lvls = np.array(normalized).reshape((len(normalized), -1))\n",
    "    U, s, V = np.linalg.svd(temp_reshaped_lvls)\n",
    "    return s\n",
    "\n",
    "def get_diversity(lvls, d_type=\"hamming\"):\n",
    "    diversity = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_diversity = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                lvl_diversity.append(div_func[d_type](lvl1, lvl2))\n",
    "        diversity.append(np.min(lvl_diversity))\n",
    "    return diversity\n",
    "\n",
    "def get_env_func(p_name, r_name, ch_perc=0.2, crop_size=28):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, add_visits=False, add_heatmap=False, add_changes=False, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), add_visits=False, add_heatmap=False, add_changes=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "def sample_data(sample_size, env_func, model, lambdas):\n",
    "    sample_info = {}\n",
    "    lvls = []\n",
    "    for name in lambdas:\n",
    "        sample_info[name] = []\n",
    "    env = DummyVecEnv([env_func])\n",
    "    for i in range(sample_size):\n",
    "        done = False\n",
    "        obs = env.reset()\n",
    "        while not done:\n",
    "            action, _ = model.predict(obs)\n",
    "            last_lvl = env.get_attr('pcgrl_env')[0]._rep.get_observation()['map']\n",
    "            obs, rewards, done, info = env.step(action)\n",
    "        lvls.append(last_lvl)\n",
    "        for name in lambdas:\n",
    "            sample_info[name].append(lambdas[name](info[0]))\n",
    "    sample_info['hdiversity'] = get_diversity(lvls, \"hamming\")\n",
    "#     sample_info['cdiversity'] = get_diversity(lvls, \"compression\")\n",
    "#     sample_info['pdiversity'] = get_pca_diversity(lvls)\n",
    "#     sample_info['sdiversity'] = get_svd_diversity(lvls)\n",
    "    return sample_info\n",
    "\n",
    "p_name = \"zelda\"\n",
    "crop_size = 22\n",
    "sample_size = 40\n",
    "rep_names = [\"narrow\", \"turtle\", \"wide\"]\n",
    "exp_name = '1'\n",
    "number_of_models = 1\n",
    "ch_perc_array = np.arange(0, 1.01, 0.1)\n",
    "result = {}\n",
    "for i in range(len(rep_names)):\n",
    "    r_name = rep_names[i]\n",
    "    if isinstance(exp_name,list):\n",
    "        e_name = exp_name[i]\n",
    "    else:\n",
    "        e_name = exp_name\n",
    "    for m_index in range(number_of_models):\n",
    "       # m_name = \"{}_{}_{}\".format(p_name, r_name, e_name)\n",
    "        m_name = \"model_1\"\n",
    "        print(\"Loading model {}\".format(m_name))\n",
    "        add_index = \"\"\n",
    "        if number_of_models > 1:\n",
    "            add_index = \"_\" + str(m_index + 1)\n",
    "        e_format = 'pkl'\n",
    "        if not os.path.exists('my_models/zelda/{}{}.{}'.format(m_name, add_index, e_format)):\n",
    "            e_format = 'pkl'\n",
    "        dir = 'my_models/zelda/{}/{}{}.{}'.format(r_name,m_name, add_index, e_format)\n",
    "        print(dir)\n",
    "        model = PPO2.load(dir)\n",
    "        if m_name not in result:\n",
    "            result[m_name] = {}\n",
    "        for ch_index in range(len(ch_perc_array)):\n",
    "            ch_perc = ch_perc_array[ch_index]\n",
    "            print(\"Testing {}_{} at change percentage of {}\".format(m_name, m_index+1, ch_perc))\n",
    "            env_func = get_env_func(p_name, r_name, ch_perc, crop_size)\n",
    "            temp_result = sample_data(sample_size, env_func, model, lambdas[p_name])\n",
    "            for name in temp_result:\n",
    "                if not(name in result[m_name]):\n",
    "                    result[m_name][name] = []\n",
    "                if number_of_models > 1:\n",
    "                    if m_index == 0:\n",
    "                        result[m_name][name].append([np.mean(temp_result[name])])\n",
    "                    else:\n",
    "                        result[m_name][name][ch_index].append(np.mean(temp_result[name]))\n",
    "                else:\n",
    "                    result[m_name][name].append(np.mean(temp_result[name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'model_1': {'player': [1.55, 1.35, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.3, 1.125, 1.025, 1.0, 1.0, 1.025, 1.0, 1.05, 1.05, 0.975, 1.0, 1.55, 1.575, 1.0, 1.075, 1.0, 1.0, 1.0, 1.025, 1.075, 1.0, 1.0], 'key': [1.275, 1.225, 1.05, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.35, 1.0, 1.0, 0.95, 0.95, 0.95, 0.95, 0.95, 1.025, 1.125, 1.0, 1.175, 1.5, 1.0, 1.0, 1.025, 1.0, 1.0, 1.025, 1.025, 1.0, 1.0], 'door': [1.475, 1.1, 0.95, 1.0, 1.0, 1.0, 1.0, 1.025, 1.0, 1.0, 1.0, 1.3, 1.15, 0.95, 1.1, 1.075, 1.025, 1.05, 1.025, 1.0, 1.0, 1.025, 2.225, 1.575, 1.025, 1.05, 1.025, 1.05, 1.0, 1.05, 1.05, 1.0, 1.0], 'regions': [2.925, 1.75, 1.2, 1.05, 1.025, 1.05, 1.05, 1.075, 1.025, 1.025, 1.025, 2.925, 1.35, 1.075, 1.175, 1.125, 1.2, 1.1, 1.15, 1.275, 1.15, 1.125, 3.6, 2.275, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.025, 1.0, 1.0], 'nearestenemy': [0.425, 1.95, 3.875, 4.325, 5.0, 3.9, 4.15, 6.85, 5.05, 4.3, 4.775, 0.35, 2.375, 3.8, 4.625, 4.35, 4.875, 4.625, 7.95, 6.575, 4.35, 12.475, 0.05, 0.675, 7.475, 9.225, 9.7, 10.375, 8.625, 8.225, 7.875, 8.0, 7.525], 'pathlength': [0.0, 5.25, 16.675, 19.825, 18.05, 18.275, 16.875, 17.15, 18.525, 17.45, 20.0, 0.0, 7.4, 14.6, 15.25, 12.775, 15.75, 15.55, 13.325, 15.775, 17.5, 15.075, 0.0, 2.225, 31.675, 30.15, 33.225, 32.15, 34.9, 31.775, 33.425, 33.7, 32.8], 'iterations': [5.725, 63.775, 428.925, 935.625, 1268.475, 1806.75, 2240.125, 1912.625, 2070.625, 3160.525, 3629.575, 4.475, 112.475, 428.625, 767.375, 1237.975, 1400.45, 1753.975, 1883.7, 2423.85, 2292.75, 3473.1, 1.0, 7.125, 11.275, 59.775, 69.6, 84.75, 11.95, 113.525, 129.775, 12.675, 10.875], 'changes': [1.0, 6.7, 12.25, 13.3, 13.875, 13.75, 15.725, 12.475, 14.4, 15.575, 14.825, 1.0, 6.225, 12.4, 17.125, 19.95, 22.975, 23.85, 26.875, 27.975, 24.325, 35.325, 1.0, 6.9, 11.0, 11.55, 11.075, 11.25, 11.275, 10.625, 11.375, 10.55, 10.275], 'player_const': [0.5, 0.725, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.375, 0.775, 0.975, 1.0, 0.95, 0.975, 1.0, 0.95, 0.975, 0.975, 1.0, 0.4, 0.5, 1.0, 0.95, 1.0, 1.0, 1.0, 0.975, 0.975, 1.0, 1.0], 'key_const': [0.55, 0.825, 0.95, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.425, 0.825, 0.95, 0.95, 0.85, 0.95, 0.95, 0.95, 0.95, 0.95, 1.0, 0.325, 0.6, 1.0, 1.0, 0.975, 1.0, 1.0, 0.975, 0.975, 1.0, 1.0], 'door_const': [0.225, 0.55, 0.95, 1.0, 1.0, 1.0, 1.0, 0.975, 1.0, 1.0, 1.0, 0.4, 0.675, 0.95, 0.875, 0.875, 0.95, 0.9, 0.95, 0.925, 0.95, 0.975, 0.225, 0.6, 0.975, 0.95, 0.975, 0.975, 1.0, 0.975, 0.975, 1.0, 1.0], 'regions_const': [0.175, 0.575, 0.875, 0.95, 0.975, 0.95, 0.95, 0.925, 0.975, 0.975, 0.975, 0.2, 0.825, 0.95, 0.85, 0.875, 0.825, 0.9, 0.85, 0.9, 0.9, 0.875, 0.175, 0.275, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.975, 1.0, 1.0], 'nearestenemy_const': [0.05, 0.2, 0.5, 0.575, 0.7, 0.55, 0.625, 0.7, 0.75, 0.6, 0.65, 0.05, 0.25, 0.375, 0.425, 0.55, 0.65, 0.525, 0.625, 0.6, 0.65, 0.7, 0.0, 0.125, 1.0, 0.95, 1.0, 1.0, 1.0, 0.975, 0.975, 1.0, 1.0], 'pathlength_const': [0.0, 0.2, 0.75, 0.825, 0.725, 0.65, 0.675, 0.75, 0.775, 0.7, 0.75, 0.0, 0.225, 0.525, 0.55, 0.5, 0.575, 0.625, 0.55, 0.65, 0.775, 0.55, 0.0, 0.075, 0.975, 0.95, 0.975, 0.975, 1.0, 0.975, 0.975, 1.0, 1.0], 'success': [0.0, 0.1, 0.425, 0.5, 0.5, 0.4, 0.425, 0.575, 0.625, 0.45, 0.425, 0.0, 0.15, 0.25, 0.375, 0.35, 0.5, 0.45, 0.5, 0.5, 0.625, 0.475, 0.0, 0.075, 0.975, 0.95, 0.975, 0.975, 1.0, 0.975, 0.975, 1.0, 1.0], 'hdiversity': [0.4584415584415584, 0.41785714285714287, 0.3542207792207792, 0.3613636363636364, 0.3344155844155844, 0.3448051948051948, 0.3185064935064935, 0.3321428571428572, 0.33766233766233766, 0.3172077922077922, 0.33441558441558444, 0.4487012987012987, 0.4422077922077922, 0.4042207792207792, 0.3957792207792208, 0.4126623376623376, 0.39772727272727276, 0.3948051948051948, 0.3922077922077922, 0.4045454545454545, 0.39675324675324675, 0.38441558441558443, 0.45681818181818185, 0.40487012987012994, 0.3662337662337662, 0.3435064935064935, 0.35064935064935066, 0.3477272727272727, 0.3571428571428572, 0.3642857142857143, 0.36331168831168836, 0.37077922077922076, 0.36038961038961037]}}\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overcoming the serialization issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "import model\n",
    "from model import FullyConvPolicySmallMap,FullyConvPolicyBigMap\n",
    "from utils import get_exp_name, max_exp_idx, load_model, make_vec_envs\n",
    "from stable_baselines import PPO2\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import zlib\n",
    "import math\n",
    "import os\n",
    "# from sklearn.decomposition import PCA\n",
    "from gym_pcgrl.envs.helper import run_dikjstra, _get_certain_tiles, get_tile_locations\n",
    "\n",
    "binary_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 48],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 48 and info['regions'] == 1]\n",
    "}\n",
    "binaryimp_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'path-imp': lambda info: info['path-imp'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathimp_const': lambda info: [0, 1][info['path-imp'] >= 20],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-imp'] >= 20 and info['regions'] == 1]\n",
    "}\n",
    "zelda_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'key': lambda info: info['key'],\n",
    "    'door': lambda info: info['door'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'nearestenemy': lambda info: info['nearest-enemy'],\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'key_const': lambda info: [0, 1][info['key'] == 1],\n",
    "    'door_const': lambda info: [0, 1][info['door'] == 1],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'nearestenemy_const': lambda info: [0, 1][info['nearest-enemy'] >= 4],\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 16],\n",
    "    'has_sol': lambda info: [0, 1][info['path-length'] >= 1 and info['nearest-enemy'] >= 4],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 16 and info['nearest-enemy'] >= 4]\n",
    "}\n",
    "sokoban_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'crate': lambda info: info['crate'],\n",
    "    'target': lambda info: info['target'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'sollength': lambda info: info['sol-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'ratio_const': lambda info: [0, 1][info['crate'] == info['target'] and info['crate'] > 0],\n",
    "    'has_sol': lambda info: [0, 1][info['sol-length'] >= 1],\n",
    "    'success': lambda info: [0, 1][info['sol-length'] >= 18]\n",
    "}\n",
    "lambdas = {\n",
    "    'binary': binaryimp_lambdas,\n",
    "    'binaryimp': binaryimp_lambdas,\n",
    "    'zelda': zelda_lambdas,\n",
    "    'sokoban': sokoban_lambdas\n",
    "}\n",
    "\n",
    "def calc_tp_count(lvl, p_size):\n",
    "    padded_lvl = np.pad(lvl, p_size, constant_values=1)\n",
    "    pattern_dict = {}\n",
    "    for (y, x), v in np.ndenumerate(lvl):\n",
    "        sy, sx = y+p_size-p_size//2, x+p_size-p_size//2\n",
    "        pattern = str(padded_lvl[sy:sy+p_size,sx:sx+p_size])\n",
    "        if not (pattern in pattern_dict):\n",
    "            pattern_dict[pattern] = 0\n",
    "        pattern_dict[pattern] += 1\n",
    "    return pattern_dict\n",
    "        \n",
    "def calc_tp_kldiv(lvl1, lvl2, p_size=3, epsilon = 1e-6):\n",
    "    p_dict = calc_tp_count(lvl1, p_size)\n",
    "    q_dict = calc_tp_count(lvl2, p_size)\n",
    "    t_patterns = set()\n",
    "    t_patterns.update(p_dict.keys())\n",
    "    t_patterns.update(q_dict.keys())\n",
    "    total_p = sum(p_dict.values())\n",
    "    total_q = sum(q_dict.values())\n",
    "    value = 0\n",
    "    for x in t_patterns:\n",
    "        p_dash = epsilon/((total_p + epsilon) * (1 + epsilon))\n",
    "        q_dash = epsilon/((total_q + epsilon) * (1 + epsilon))\n",
    "        if x in p_dict:\n",
    "            p_dash = (p_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        if x in q_dict:\n",
    "            q_dash = (q_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        value += p_dash * math.log(p_dash/q_dash)\n",
    "    return value\n",
    "\n",
    "def calc_longest_path(map):\n",
    "    map_locations = get_tile_locations(map, [0, 1])\n",
    "    empty_tiles = _get_certain_tiles(map_locations, passable_values)\n",
    "    final_visited_map = np.zeros((len(map), len(map[0])))\n",
    "    final_value = 0\n",
    "    final_loc = (0,0)\n",
    "    final_map = None\n",
    "    for (x,y) in empty_tiles:\n",
    "        if final_visited_map[y][x] > 0:\n",
    "            continue\n",
    "        dikjstra_map, visited_map = run_dikjstra(x, y, map, [0])\n",
    "        final_visited_map += visited_map\n",
    "        (my,mx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        dikjstra_map, _ = run_dikjstra(mx, my, map, passable_values)\n",
    "        (fy, fx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        max_value = np.max(dikjstra_map)\n",
    "        if max_value > final_value:\n",
    "            final_value = max_value\n",
    "            final_loc = (fx, fy)\n",
    "            final_map = dikjstra_map\n",
    "    path = [final_loc]\n",
    "    current = final_loc\n",
    "    while final_map[current[1]][current[0]] != 0:\n",
    "        small = (current[0], current[1])\n",
    "        for (dx,dy) in [(0,1), (0,-1), (1,0), (-1,0)]:\n",
    "            new = (current[0] + dx, current[1] + dy)\n",
    "            if final_map[new[1]][new[0]] == -1 or new[0] < 0 or new[1] < 0 or new[0] >= final_map.shape[1] or new[1] >= final_map.shape[0]:\n",
    "                continue\n",
    "            if final_map[new[1]][new[0]] < final_map[small[1]][small[0]]:\n",
    "                small = new\n",
    "        path.append(small)\n",
    "        current = small\n",
    "    return path\n",
    "\n",
    "def calc_hamming(lvl1, lvl2):\n",
    "    return np.clip(abs(lvl1 - lvl2), 0, 1).sum()/(lvl1.shape[0] * lvl1.shape[1])\n",
    "\n",
    "def calc_compression(lvl1, lvl2):\n",
    "    c_1 = zlib.compress(lvl1.tobytes(), level=9)\n",
    "    c_2 = zlib.compress(lvl2.tobytes(), level=9)\n",
    "    cb = zlib.compress(lvl1.tobytes() + lvl2.tobytes(), level=9)\n",
    "    return (len(cb) - min(len(c_1), len(c_2))) / max(len(c_1), len(c_2))\n",
    "\n",
    "div_func = {\n",
    "    'hamming': lambda lvl1, lvl2: calc_hamming(lvl1, lvl2),\n",
    "    'tpkldiv': lambda lvl1, lvl2: calc_tp_kldiv(lvl1, lvl2),\n",
    "    'compression': lambda lvl1, lvl2: calc_compression(lvl1, lvl2)\n",
    "}\n",
    "\n",
    "def get_pca_diversity(lvls):\n",
    "    pca2d = PCA(2)\n",
    "    temp_reshaped_lvls = np.array(lvls).reshape((len(lvls), -1))\n",
    "    projected_lvls = pca2d.fit_transform(temp_reshaped_lvls)\n",
    "    variance = np.var(projected_lvls, axis=0)\n",
    "    return [np.mean(variance)]\n",
    "\n",
    "def get_svd_diversity(lvls):\n",
    "    normalized = []\n",
    "    for i in range(len(lvls)):\n",
    "        normalized.append(lvls[i] / np.linalg.norm(lvls[i]))\n",
    "    temp_reshaped_lvls = np.array(normalized).reshape((len(normalized), -1))\n",
    "    U, s, V = np.linalg.svd(temp_reshaped_lvls)\n",
    "    return s\n",
    "\n",
    "def get_diversity(lvls, d_type=\"hamming\"):\n",
    "    diversity = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_diversity = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                lvl_diversity.append(div_func[d_type](lvl1, lvl2))\n",
    "        diversity.append(np.min(lvl_diversity))\n",
    "    return diversity\n",
    "\n",
    "def get_env_func(p_name, r_name, ch_perc=0.2, crop_size=28):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, add_visits=False, add_heatmap=False, add_changes=False, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), add_visits=False, add_heatmap=False, add_changes=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "def sample_data(sample_size, env_func, model, lambdas):\n",
    "    sample_info = {}\n",
    "    lvls = []\n",
    "    for name in lambdas:\n",
    "        sample_info[name] = []\n",
    "    env = DummyVecEnv([env_func])\n",
    "    for i in range(sample_size):\n",
    "        done = False\n",
    "        obs = env.reset()\n",
    "        while not done:\n",
    "            if model is None:\n",
    "                action = [env.action_space.sample()]\n",
    "            else:\n",
    "                action, _ = model.predict(obs)\n",
    "            last_lvl = env.get_attr('pcgrl_env')[0]._rep.get_observation()['map']\n",
    "            obs, rewards, done, info = env.step(action)\n",
    "        lvls.append(last_lvl)\n",
    "        for name in lambdas:\n",
    "            sample_info[name].append(lambdas[name](info[0]))\n",
    "    sample_info['hdiversity'] = get_diversity(lvls, \"hamming\")\n",
    "#     sample_info['cdiversity'] = get_diversity(lvls, \"compression\")\n",
    "#     sample_info['pdiversity'] = get_pca_diversity(lvls)\n",
    "#     sample_info['sdiversity'] = get_svd_diversity(lvls)\n",
    "    return sample_info\n",
    "\n",
    "def get_result_dict(ch_perc_array, lambdafuncs):\n",
    "    result = {}\n",
    "    for name in lambdafuncs:\n",
    "        result[name] = []\n",
    "        for _ in ch_perc_array:\n",
    "            result[name].append([])\n",
    "    result['hdiversity'] = []        \n",
    "    for _ in ch_perc_array:\n",
    "        result['hdiversity'].append([])\n",
    "    return result\n",
    "\n",
    "def get_change_percentage_data(model, p_name, r_name, crop_size, sample_size, ch_perc_array, lambdafuncs, result):\n",
    "    for ch_index in range(len(ch_perc_array)):\n",
    "        ch_perc = ch_perc_array[ch_index]\n",
    "        print(\"Testing {}_{} at change percentage of {}\".format(p_name, r_name, ch_perc))\n",
    "        env_func = get_env_func(p_name, r_name, ch_perc, crop_size)\n",
    "        temp_result = sample_data(sample_size, env_func, model, lambdafuncs)\n",
    "        for name in temp_result:\n",
    "            result[name][ch_index].append(np.mean(temp_result[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_name = \"zelda\"\n",
    "ch_perc_array = np.arange(0, 1.01, 0.1)\n",
    "lambdafuncs = lambdas[p_name]\n",
    "\n",
    "result = {}\n",
    "result['{}_narrow'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "result['{}_turtle'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "result['{}_wide'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'zelda_narrow': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'zelda_turtle': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}, 'zelda_wide': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('{}_results.json'.format(p_name)) as fp:\n",
    "    result = json.load(fp)\n",
    "    \n",
    "print(result)\n",
    "#result['{}_random'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "result['{}_turtle random'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "# result['{}_wide random'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing zelda_narrow\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing zelda_narrow at change percentage of 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:51: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing zelda_narrow at change percentage of 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:52: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:53: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:54: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing zelda_narrow at change percentage of 0.2\n",
      "Testing zelda_narrow at change percentage of 0.30000000000000004\n",
      "Testing zelda_narrow at change percentage of 0.4\n",
      "Testing zelda_narrow at change percentage of 0.5\n",
      "Testing zelda_narrow at change percentage of 0.6000000000000001\n",
      "Testing zelda_narrow at change percentage of 0.7000000000000001\n",
      "Testing zelda_narrow at change percentage of 0.8\n",
      "Testing zelda_narrow at change percentage of 0.9\n",
      "Testing zelda_narrow at change percentage of 1.0\n",
      "Testing zelda_turtle\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing zelda_turtle at change percentage of 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:51: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:52: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:53: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:54: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing zelda_turtle at change percentage of 0.1\n",
      "Testing zelda_turtle at change percentage of 0.2\n",
      "Testing zelda_turtle at change percentage of 0.30000000000000004\n",
      "Testing zelda_turtle at change percentage of 0.4\n",
      "Testing zelda_turtle at change percentage of 0.5\n",
      "Testing zelda_turtle at change percentage of 0.6000000000000001\n",
      "Testing zelda_turtle at change percentage of 0.7000000000000001\n",
      "Testing zelda_turtle at change percentage of 0.8\n",
      "Testing zelda_turtle at change percentage of 0.9\n",
      "Testing zelda_turtle at change percentage of 1.0\n",
      "Testing zelda_wide\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing zelda_wide at change percentage of 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:51: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing zelda_wide at change percentage of 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:52: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:53: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "c:\\Python37\\lib\\site-packages\\ipykernel_launcher.py:54: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing zelda_wide at change percentage of 0.2\n",
      "Testing zelda_wide at change percentage of 0.30000000000000004\n",
      "Testing zelda_wide at change percentage of 0.4\n",
      "Testing zelda_wide at change percentage of 0.5\n",
      "Testing zelda_wide at change percentage of 0.6000000000000001\n",
      "Testing zelda_wide at change percentage of 0.7000000000000001\n",
      "Testing zelda_wide at change percentage of 0.8\n",
      "Testing zelda_wide at change percentage of 0.9\n",
      "Testing zelda_wide at change percentage of 1.0\n"
     ]
    }
   ],
   "source": [
    "crop_size = 22\n",
    "sample_size = 40\n",
    "r_names = [\"narrow\", \"turtle\", \"wide\"]\n",
    "for i in range(len(r_names)):\n",
    "    r_name = r_names[i]\n",
    "    print(\"Testing {}_{}\".format(p_name, r_name))\n",
    "    model = None\n",
    "    model = PPO2.load('my_models/{}/{}/model_1.pkl'.format(p_name, r_name))\n",
    "    res_name = r_name\n",
    "    if model is None:\n",
    "        res_name = \"random\"\n",
    "    get_change_percentage_data(model, p_name, r_name, crop_size, sample_size, ch_perc_array, lambdafuncs, result['{}_{}'.format(p_name, res_name)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zelda\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "print(p_name)\n",
    "with open('{}_results.json'.format(p_name), 'w') as fp:\n",
    "    json.dump(result, fp)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single Model Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def get_data(results, name):\n",
    "    output = {}\n",
    "    for n in results:\n",
    "        output[n] = results[n][name]\n",
    "    return output\n",
    "\n",
    "def plt_dict(p_dict, y_title, file_name):\n",
    "    plt.figure()\n",
    "    names = []\n",
    "    for name in p_dict:\n",
    "        plt.plot(np.array(ch_perc_array),p_dict[name])\n",
    "        names.append(name)\n",
    "    plt.legend(names)\n",
    "    plt.xlim(0.0,1.0)\n",
    "    plt.xticks(np.array(ch_perc_array), rotation=90)\n",
    "    plt.xlabel('change percentage')\n",
    "    plt.ylabel(y_title)\n",
    "    plt.savefig('graphs/' + file_name + \".pdf\")\n",
    "    for n in lambdas[p_name]:\n",
    "        plt_dict(get_data(result, n), n, '{}_{}_{}'.format(p_name, exp_name, n))\n",
    "    plt_dict(get_data(result, 'hdiversity'), 'hamming diversity', '{}_{}_{}'.format(p_name,exp_name,'hammingdiversity'))\n",
    "    plt_dict(get_data(result, 'cdiversity'), 'compression diversity', '{}_{}_{}'.format(p_name,exp_name,'compressiondiversity'))\n",
    "    plt_dict(get_data(result, 'pdiversity'), 'pca diversity', '{}_{}_{}'.format(p_name,exp_name,'pcadiversity'))\n",
    "    plt_dict(get_data(result, 'sdiversity'), 'svd diversity', '{}_{}_{}'.format(p_name,exp_name,'svddiversity'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paper Graphs with multi models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} player\n",
      "zelda_narrow {'zelda_narrow': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_wide': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_wide': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_wide': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], 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[[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} key\n",
      "zelda_narrow {'zelda_narrow': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'zelda_wide': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'zelda_wide': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'zelda_wide': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} door\n",
      "zelda_narrow {'zelda_narrow': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'zelda_wide': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'zelda_wide': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'zelda_wide': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} regions\n",
      "zelda_narrow {'zelda_narrow': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'zelda_turtle': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'zelda_wide': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'zelda_turtle': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'zelda_wide': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'zelda_turtle': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'zelda_wide': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} nearestenemy\n",
      "zelda_narrow {'zelda_narrow': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'zelda_turtle': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'zelda_wide': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'zelda_turtle': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'zelda_wide': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'zelda_turtle': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'zelda_wide': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} pathlength\n",
      "zelda_narrow {'zelda_narrow': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'zelda_turtle': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'zelda_wide': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'zelda_turtle': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'zelda_wide': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'zelda_turtle': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'zelda_wide': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} iterations\n",
      "zelda_narrow {'zelda_narrow': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'zelda_turtle': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'zelda_wide': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'zelda_turtle': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'zelda_wide': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'zelda_turtle': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'zelda_wide': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} changes\n",
      "zelda_narrow {'zelda_narrow': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'zelda_turtle': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'zelda_wide': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'zelda_turtle': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'zelda_wide': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'zelda_turtle': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'zelda_wide': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} player_const\n",
      "zelda_narrow {'zelda_narrow': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_wide': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_wide': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_wide': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} key_const\n",
      "zelda_narrow {'zelda_narrow': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'zelda_wide': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'zelda_wide': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'zelda_wide': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} door_const\n",
      "zelda_narrow {'zelda_narrow': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'zelda_wide': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'zelda_wide': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'zelda_turtle': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'zelda_wide': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} regions_const\n",
      "zelda_narrow {'zelda_narrow': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'zelda_turtle': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'zelda_wide': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'zelda_turtle': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'zelda_wide': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'zelda_turtle': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'zelda_wide': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} nearestenemy_const\n",
      "zelda_narrow {'zelda_narrow': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'zelda_turtle': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'zelda_wide': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'zelda_turtle': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'zelda_wide': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'zelda_turtle': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'zelda_wide': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} pathlength_const\n",
      "zelda_narrow {'zelda_narrow': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'zelda_turtle': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'zelda_wide': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'zelda_turtle': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'zelda_wide': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'zelda_turtle': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'zelda_wide': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} success\n",
      "zelda_narrow {'zelda_narrow': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'zelda_turtle': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'zelda_wide': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'zelda_turtle': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'zelda_wide': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'zelda_turtle': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'zelda_wide': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'zelda_narrow': {'player': [[1.475], [1.25], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.35], [1.05], [1.05], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door': [[1.675], [1.15], [1.0], [1.0], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions': [[3.775], [1.55], [1.075], [1.0], [1.125], [1.0], [1.05], [1.075], [1.025], [1.025], [1.025]], 'nearestenemy': [[0.375], [1.825], [4.25], [4.325], [3.775], [5.075], [6.475], [5.95], [5.3], [5.25], [4.4]], 'pathlength': [[0.0], [7.275], [15.675], [19.725], [17.325], [17.4], [17.125], [17.2], [18.25], [19.9], [17.9]], 'iterations': [[3.65], [99.225], [465.225], [865.7], [1433.65], [1559.975], [2241.4], [2406.95], [2212.2], [2578.45], [3060.85]], 'changes': [[1.0], [6.8], [11.85], [11.825], [12.9], [14.775], [12.95], [13.425], [14.55], [14.425], [14.525]], 'player_const': [[0.475], [0.75], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.425], [0.875], [0.95], [1.0], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.275], [0.75], [0.85], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'regions_const': [[0.125], [0.625], [0.925], [1.0], [0.875], [1.0], [0.95], [0.925], [0.975], [0.975], [0.975]], 'nearestenemy_const': [[0.05], [0.25], [0.5], [0.75], [0.575], [0.7], [0.625], [0.525], [0.7], [0.75], [0.65]], 'pathlength_const': [[0.0], [0.3], [0.65], [0.775], [0.725], [0.725], [0.6], [0.75], [0.775], [0.825], [0.85]], 'has_sol': [[0.0], [0.225], [0.4], [0.75], [0.575], [0.675], [0.625], [0.525], [0.7], [0.75], [0.65]], 'success': [[0.0], [0.2], [0.325], [0.575], [0.475], [0.5], [0.4], [0.45], [0.55], [0.6], [0.575]], 'hdiversity': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]]}, 'zelda_turtle': {'player': [[1.4], [1.075], [1.05], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key': [[1.475], [1.05], [0.925], [1.0], [0.975], [1.05], [1.0], [1.05], [1.025], [1.025], [0.95]], 'door': [[1.025], [0.85], [1.075], [1.15], [1.075], [1.05], [1.025], [1.0], [1.0], [1.025], [1.125]], 'regions': [[2.45], [1.275], [1.075], [1.225], [1.1], [1.05], [1.125], [1.1], [1.125], [1.275], [1.1]], 'nearestenemy': [[0.625], [5.375], [6.0], [7.375], [5.25], [7.575], [4.6], [8.775], [6.75], [11.325], [8.225]], 'pathlength': [[0.25], [6.7], [12.9], [12.45], [13.6], [16.725], [15.425], [14.6], [15.2], [15.275], [14.825]], 'iterations': [[5.95], [143.2], [444.9], [845.7], [1047.725], [1440.275], [1571.1], [1608.225], [2547.525], [1910.925], [3231.55]], 'changes': [[1.0], [6.425], [11.475], [17.225], [20.075], [21.65], [26.35], [28.35], [27.025], [31.25], [37.225]], 'player_const': [[0.325], [0.9], [0.95], [1.0], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'key_const': [[0.375], [0.8], [0.925], [0.925], [0.925], [0.95], [1.0], [0.95], [0.975], [0.95], [0.95]], 'door_const': [[0.325], [0.575], [0.8], [0.825], [0.85], [0.875], [0.925], [0.9], [0.95], [0.9], [0.9]], 'regions_const': [[0.275], [0.85], [0.925], [0.85], [0.9], [0.95], [0.875], [0.9], [0.9], [0.9], [0.9]], 'nearestenemy_const': [[0.1], [0.4], [0.6], [0.525], [0.675], [0.55], [0.5], [0.7], [0.675], [0.75], [0.55]], 'pathlength_const': [[0.0], [0.2], [0.5], [0.45], [0.45], [0.625], [0.6], [0.625], [0.55], [0.675], [0.65]], 'has_sol': [[0.025], [0.2], [0.475], [0.45], [0.575], [0.5], [0.45], [0.625], [0.65], [0.7], [0.525]], 'success': [[0.0], [0.075], [0.35], [0.325], [0.425], [0.4], [0.425], [0.525], [0.475], [0.6], [0.45]], 'hdiversity': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]]}, 'zelda_wide': {'player': [[1.8], [1.575], [1.0], [1.0], [1.025], [1.025], [1.0], [1.025], [1.0], [1.0], [1.0]], 'key': [[1.275], [1.85], [1.0], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door': [[2.2], [1.65], [1.025], [1.05], [1.1], [1.1], [1.0], [1.075], [1.0], [1.0], [1.025]], 'regions': [[3.4], [2.05], [1.025], [1.0], [1.0], [1.075], [1.0], [1.05], [1.0], [1.0], [1.0]], 'nearestenemy': [[0.075], [1.975], [6.75], [7.375], [8.125], [8.75], [8.05], [7.975], [8.2], [7.55], [8.3]], 'pathlength': [[0.0], [4.4], [30.375], [30.5], [34.05], [30.975], [32.45], [30.875], [32.85], [33.35], [35.075]], 'iterations': [[1.0], [7.675], [11.225], [53.5], [99.85], [180.25], [13.25], [315.875], [11.65], [11.325], [13.9]], 'changes': [[1.0], [6.9], [10.45], [9.3], [11.4], [11.775], [11.575], [10.375], [11.0], [10.775], [12.95]], 'player_const': [[0.3], [0.575], [1.0], [1.0], [0.975], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0]], 'key_const': [[0.325], [0.5], [0.95], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'door_const': [[0.3], [0.575], [0.975], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'regions_const': [[0.05], [0.425], [0.975], [1.0], [1.0], [0.975], [1.0], [0.95], [1.0], [1.0], [1.0]], 'nearestenemy_const': [[0.0], [0.25], [0.95], [1.0], [0.975], [0.95], [1.0], [0.925], [1.0], [1.0], [0.975]], 'pathlength_const': [[0.0], [0.15], [0.925], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'has_sol': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [1.0], [0.925], [1.0], [1.0], [0.975]], 'success': [[0.0], [0.15], [0.9], [0.95], [0.95], [0.925], [0.975], [0.925], [1.0], [1.0], [0.975]], 'hdiversity': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]]}, 'zelda_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'key': [[], [], [], [], [], [], [], [], [], [], []], 'door': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'key_const': [[], [], [], [], [], [], [], [], [], [], []], 'door_const': [[], [], [], [], [], [], [], [], [], [], []], 'regions_const': [[], [], [], [], [], [], [], [], [], [], []], 'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []], 'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} hdiversity\n",
      "zelda_narrow {'zelda_narrow': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]], 'zelda_turtle': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]], 'zelda_wide': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_turtle {'zelda_narrow': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]], 'zelda_turtle': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]], 'zelda_wide': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "zelda_wide {'zelda_narrow': [[0.46655844155844156], [0.43019480519480524], [0.36233766233766235], [0.35227272727272724], [0.34220779220779224], [0.3425324675324675], [0.34350649350649354], [0.3487012987012987], [0.3334415584415584], [0.33279220779220775], [0.32954545454545453]], 'zelda_turtle': [[0.45], [0.423051948051948], [0.4146103896103896], [0.40681818181818186], [0.4042207792207792], [0.4029220779220779], [0.3850649350649351], [0.387987012987013], [0.40064935064935064], [0.37110389610389605], [0.39448051948051954]], 'zelda_wide': [[0.4603896103896103], [0.4155844155844156], [0.3675324675324675], [0.3623376623376623], [0.3571428571428572], [0.3474025974025974], [0.3600649350649351], [0.3681818181818182], [0.3467532467532468], [0.3613636363636364], [0.3418831168831169]], 'zelda_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6koEHdOjQoa430p49e7J69WoqKiooKyvjp59+YuzYsYfcrr5Ro0Zx/fXXs2HDBvr06cOePXvYvn07/fr1c+n7aC4O4+CLDV/w7PJnKawoZGq/qdww7IYWU2F5MMkxybwf/T7zt8/nhdQXuPv3u3ljxRtcn3g9J/c8eZ8kl1eWt0+xT22/9EG+QSRGJjJp2CSSopMYEj6kWUamai6R7SI5vdfpnN7rdIwxbC7azKKsRSzOWszczXOZvX42AAPCBtQlh+GdhzdabwFWT7O1RUBrC9YCexPAxNiJreJOp9ZEk8ERCg8P59hjj2XIkCFMmjSJqVOnMmTIEOLi4hg27OD96FxxxRVce+21dRXItSIjI5kxYwYXXnghFRUVADz88MMtIhmszlvNI4sfIS03jYTIBF49+VUGhntXJfbREBHGdxvP2K5j+XHLj7xkf4nbfruNgWEDObfvuWwo3EBKdgobd28ErIP/8KjhTO41maTOSQwOH+xVB//GiAhxneKI6xTHhQMupNpRzZq8NXVFSh+s+YAZq2bgZ/MjMSqR5OhkRnUZxeDwwfjafPlz9591VwDrCtYBkBiZyJ0j72RCzwmaALyY3k3Uyrjzc9ldsZsXUl/gk7WfEBoYyi0jbuHM3me2uCKhw1XjqOGbP7/hZfvLZJZk0s63HcM7D6+722dg+MBW28q1rLqM1J2pLMpexKIdi+rueAr2CyayXSR/7v4TgGFRw5jYcyIn9zxZE4AHtb27iZRbOYyDz9Z/xnPLn2N35W4uGngR1yVeR0f/jp4OzS18bD6c0fsMTo07lW1F2+jRsUebGXozyDeIMV3HMKbrGBgBBeUFLMlewqKsRWSVZDG131RNAC1U2/gFt2K1V3YGgzEGh3GQW5pLlaOKakd13d/a6dpH/Xn7Tze2vMpRxcpdK1mVt4rhUcO5O/lu+of19/Cn4Bl+Nj96hfTydBgeFRoYyimxp3BK7CmeDkUdJZclAxEJBOYBAc79fGqMuV9E4oCPgHBgGXCpMabSVXG0JttLtlNcWQzGefB3JoD6svdkc8GsC5p1vz7ig5/ND1+bL742X0ICQnhk7COc3uv0Vt0PjlJtiSuvDCqAE40xJSLiB/wuIt8CtwLPGGM+EpFXgb8Cr7gwjlahtn/2QJ9AgnyDEBEEqTsY106XBZRx76h76w7e9Q/ihzOv/vPWXgeglHJhMjDWKWuJ86mf82GAE4GLnPPfAR5Ak8EhlVWXYYwhIiiCjgEHL5vP9ctlav+pboxMKdUauPSUT0R8RMQO5AA/ABuBQmNMtXOVTKDBDkdE5GoRSRGRlNzcXFeG2SKUVpUCuLWvHKVU2+HSZGCMqTHGJALdgGOAAYex7WvGmCRjTFJkZMvra/60006jsLDwgPkPPPBAo/0YHUxZdRn+Pv5t5q4VpZR7ueXIYowpFJFfgNFAiIj4Oq8OugHb3RGDu33zzTfN9lrGGEqrS72iW2WlVOvksisDEYkUkRDndBAwAVgD/AKc71ztcuALV8XgSk8++STPP/88ALfccgsnnngiAD///DMXX3wxsbGx7Nq1C4D//Oc/9OvXj7Fjx7J27dq619i4cSOnnnoqI0aMYNy4cWRkZDS4r8qaSmocNbTz1SIipZRruPLKIAZ4R0R8sJLOJ8aYOSKyGvhIRB4GUoE3j3ZHjy95vK7zq+YyIGwAdx1z10GXjxs3jqeeeoobb7yRlJQUKioqqKqqYv78+YwfP54//vgDgGXLlvHRRx9ht9uprq5m+PDhjBgxAoCrr76aV199lb59+7J48WKuu+46fv755wP2VVrtrC/QZKCUchFX3k2UDhzQSY8xZhNW/UGLNmLECJYtW0ZRUREBAQEMHz6clJQU5s+fz/PPP8+jjz4KwPz58znnnHNo1846kJ955pkAlJSUsGDBAqZMmVL3mrV9Eu2vtLoUH5uPW3u2VEq1La2iNrKxM3hX8fPzIy4ujhkzZjBmzBji4+P55Zdf2LBhQ5P6BnI4HISEhGC32w+5bmlVaV3bAqWUcgVtTXQUxo0bx/Tp0xk/fjzjxo3j1VdfZdiwYfsctMePH8/nn39OWVkZxcXFfPXVVwB07NiRuLg4Zs2aBViVxGlpaQfso9pRTWVNpRYRKaVcSpPBURg3bhxZWVmMHj2azp07ExgYyLhx4/ZZZ/jw4UybNo2EhAQmTZrEyJEj65Z98MEHvPnmmyQkJDB48GC++OLAuvSy6jJA2xcopVxLu7D2cjv37CSvLI8B4U0bn7atfC5KqUM7nC6s9crAy5VWlxLoG6j9AymlXEqPMF7MYRyUVZcR5Nf48IJKKXW0WnQyaAlFXEejvLocY0yTK49b++ehlHKdFpsMAgMDycvLa9UHwMNpbGaMIS8vj8DAQFeHpZRqhVpsO4Nu3bqRmZlJa+7RNL88nypHFbbcpuXswMBAunXr5uKolFKtUYtNBrWNvlorYwwnzTqJkdEjeXzE454ORynVyrXYYqLWbseeHeSW5TIs6oAePZRSqtlpMvBSqTmpACRGJXo2EKVUm6DJwEvZc+y0821H35C+ng5FKdUGaDLwUvYcO/GR8fjYfDwdilKqDdBk4IVKKktYX7he6wuUUm6jycALpe9Kx2EcJEYmejoUpVQbocnAC9lz7NjERnxkvKdDUUq1EZoMvJA9x07fkL4E+wd7OhSlVBuhycDL1DhqSN+VrreUKqXcymXJQES6i8gvIrJaRFaJyE3O+Q+IyHYRsTsfp7kqhpZoQ+EG9lTt0WSglHIrV3ZHUQ3cZoxZLiIdgGUi8oNz2TPGmOku3HeLVdfYTCuPlVJu5LJkYIzJArKc08Uisgbo6qr9tRb2XDuRQZF0DdaPSinlPm6pMxCRWGAYsNg56wYRSReRt0Qk9CDbXC0iKSKS0pp7Jt2fPcdOYlQiIuLpUJRSbYjLk4GIBAOzgZuNMUXAK0BvIBHryuGphrYzxrxmjEkyxiRFRka6OkyvkFOaw/aS7VpEpJRyO5cmAxHxw0oEHxhj/gdgjNlpjKkxxjiA14FjXBlDS2LPsQPaOZ1Syv1ceTeRAG8Ca4wxT9ebH1NvtXOAla6KoaWx59oJ8AlgYNhAT4eilGpjXHk30bHApcAKEbE7590NXCgiiYABNgPXuDCGFsWeY2dIxBD8fPw8HYpSqo1x5d1EvwMN1YJ+46p9tmRl1WWsyVvD5YMv93QoSqk2SFsge4lVu1ZRbaq1p1KllEdoMvAS9lw7AAmRCZ4NRCnVJmky8BL2HDtxneIICQzxdChKqTZIk4EXcBgH9ly7ti9QSnmMJgMvsLloM7srdmt9gVLKYzQZeIHaxmYJUVpfoJTyDE0GXsCeYyckIIS4jnGeDkUp1UZpMvACqTmpJEZq53RKKc/RZOBhBeUFbC7arEVESimP0mTgYWm5aQBaeayU8ihNBh6WmpOKr82XweGDPR2KUqoN02TgYfYcO4PCBhHoG+jpUJRSbViTOqoTkX7AHUDP+tsYY050UVxtQlVNFavyVjG1/1RPh6KUauOa2mvpLOBVrMFoalwXTtuyJn8NFTUVWl+glPK4piaDamPMKy6NpA1KzUkF0G4olFIe12gyEJEw5+RXInId8BlQUbvcGJPvwthavbTcNLoGdyWyXdsY41kp5b0OdWWwDGtEstrWUHfUW2aAXq4Iqi0wxpCak0pyTLKnQ1FKqcaTgTEmDkBEAo0x5fWXiYje/nIUtpdsZ1fZLoZFan2BUsrzmnpr6YImzlNNVFdfEJXo2UCUUopD1xlEA12BIBEZxt7ioo5Au0Ns2x14F+iMVaT0mjHmOWc9xMdALLAZmGqMKTiK99Ai2XPsBPsF0yekj6dDUUqpQ9YZnAJcAXQDnq43vxi4+xDbVgO3GWOWi0gHYJmI/OB8vZ+MMY+JyD+BfwJ3HUHsLZo91058ZDw+Nh9Ph6KUUoesM3gHeEdEzjPGzD6cFzbGZAFZzuliEVmDdZVxFnC8c7V3gF9pY8mguLKY9QXrObnHyZ4ORSmlgKa3M+gpIrfuN283sMwYYz/UxiISCwwDFgOdnYkCIBurGKmhba4Grgbo0aNHE8NsGVbkrsBgtL5AqVYks6CUeet2MW9dLllF5Zw0IIrJ8TH0jgz2dGhN0tRkkOR8fOV8fjqQDlwrIrOMMU8cbEMRCQZmAzcbY4rq99lvjDEiYhrazhjzGvAaQFJSUoPrtFSpuanYxEZ8ZLynQzksDofh3YWb+SJtByf0j2LayO507qg3lam2qbSymsWb8vltXS7z1ueyKXcPAF06BRLVMZBnflzH0z+sY2BMR06Pj+G0oTHERbT3cNQHJ8Yc+jgrIvOA04wxJc7nwcDXwKlYVweDDrKdHzAH+N4Y87Rz3lrgeGNMlojEAL8aY/o3tv+kpCSTkpJyGG/Lu1019yoKKwqZdcYsT4fSZJkFpdz5aToLNuYRG96OzXml+NiEkwdGcVFyT8b1icBm08F5VOtljCEju5h5zoP/0j8LqKxxEOBrY1SvcMb3i+S4fhH0jgxGRMjeXc43K7L4ekUWy7ZY98gM7tKRyfExnD60Cz3CG70Hp1mIyDJjTFJT1m3qlUEU9VoeA1VYxT1lIlLR0AZiXQK8CaypTQROXwKXA485/37RxBhahWpHNem56ZzR+wxPh9IkxhhmpWTy7zmrMcbw2LlDmTayO1vySvlwyVZmLcvk+1U76RHWjguO6c6UEd2J7BDg6bCVahYFeyqZv2EXv63NZf76XHKKrcNdv87BXDa6J+P7RXJMXBiBfgfeCBLdKZArx8Zx5dg4dhSW1SWGJ75byxPfrSW+WycmD7WuGLqHuT4xHEpTrwzuBc5h74H7DKyD+lNYt4xe3MA2Y4H5wArA4Zx9N1a9wSdAD2AL1q2ljXZr0ZquDDLyM5jy1RQeG/cYk3tN9nQ4jcopKuf//reCnzJyGNUrjCfPTzjgR1tRXcN3K7OZuXgri//Mx89HmDg4mouTezC6V7gO5alalOoaB6nbCq2z/3W5pG/fjTHQKciPsX0jOK5vJOP6RRDTKeiI95FZUGolhvQs0jJ3A5DQPYTTh8ZwWnwMXUOO/LX3dzhXBk1KBs4XHQmMcT79wxjjtqNza0oGH2Z8yCOLH+H7876nS3AXT4dzUHPSd3DP5yspq6zhrlMHcMWY2EMWA23IKWbm4m3MXp7J7rIqekW056LkHpw3vBuh7f3dFLlSh2dbfinz1lsH/wUb8iiuqMYmkNg9xFn0E0l8txB8XFAMui2/lDnpWXy9YgcrtxcBMKxHCKfHd+G0odFHlXTAdcnAB+vOn/rjGWw9oggPU2tKBnfNu4uU7BR+nPKjV541F+yp5L4vV/FV2g4Suofw1JQE+kQd3t0Q5VU1fJ2excwlW1m2pQB/XxunDYnm4lE9SeoZ6pXvW7UdjVX8ju8Xyfh+kRzbO4JO7fzcGtfmXXv42nnFsDrLSgxJPUOZ7Kx8PpKbNZo9GYjIP4D7gZ1Y4xkI1s1AbrkdpjUlg1M+PYUhEUN46vinPB3KAX7O2Mlds1dQWFrJTSf15drjeuPrc3SD4WVkFzFz8VY+W76d4opq+kYFc3FyD84Z3o1OQe79Z1Nt0+FW/HqDTbklfJ1u1TFkZBcjAiN7hnF6QgynDokmqkPTEoMrksEGINkYk9ekCJpZa0kGO/fs5ORPT+bOkXdy6aBLPR1OneLyKh6es4aPU7YxILoDT01NYHCXTs26j9LKar5K28HMxVtJy9xNoJ+NM+K7cFFyDxK7h3jNP6FqHRqr+B3fN7LRil9vsyGnmK/Ts5mTvoP1OSWIQHJcGJPju3Dq4OhGb9hwRTL4BZhgjKlu8jtoRq0lGXy/+Xtu/+12Ppz8IUMihng6HAAWbNzFHbPSydpdxrXH9eamk/sS4Ovaf5CV23fzweKtfGHfTmllDYNiOnJRcg/OHtaV4ICm3uCmlKWqxsGm3D1kZBexJquYhRt3uaTi1xus21nMnPQs5qTvYFPuHmwCo3qFc3p8F04Z3Jnw4H0TgyuSwZtAf6y2BfUHt3n6oBs1o9aSDB5f8jifrvuUBRctwM/m2SKSssoanvg+g7f/2ExcRHumT0lgRM9Qt8ZQXF7FF/YdfLB4K2uyimjv78OZiV25OLkHQ7o275WJavmMMWQXlZORVUxGdjFrs4vIyC5mY24JVTXWcczXJsR361RX9p/goopfTzPGsHZnMV+nZzEnPYs/d+3BxyaM6R3O5KExnDI4mtD2/i5JBvcfJKAHD/M9HJHWkgwunHMhgb6BvH3q2x6NI3VrAbd9ksamXXu4Ykwsd57an3b+njsjN8Zg31bIzMVb+Sp9B+VVDhK6deKi5B6ckdDFo7Epzygur2LdztqDfrEzARRRVL63cCKmUyD9ozvQP7oDA6M70j+6A70i27v8ytbbGGNYnVVUlxi25pfiaxPG9Ingvb8mN//dRAAi0s4YU3rEUR+h1pAMyqrLGDNzDFcMuYKbht/kkRgqqx0899M6Xvl1I9EdA3lySgLH9onwSCwHs7usis+WZzJzyVbW7SyhQ4Av5wzvykXJPRgQ3dHT4almVl3j4M9de1jjPNNfm20lgMyCsrp1ggN86w76A6I7MCC6I/07d3D73T4tgTGGVTuK6oqS/vjnSc1+ZTAaqzVxsDGmh4gkANcYY647utCbpjUkg6XZS7ny+yt56aSXGN9tvNv3vyariFs+tpORXcyUEd2494xBdAz03n8mYwwpWwqYuXgrX6/IorLawYieoVyc3IPThsa0iIo/tZcxhp1FFWTUO+BnZBezMaeEyhqrTaqPTegd2Z7+0R0ZEN2B/p2tBNAtNEhvMDgCxhhsNluzd0fxLNbYBl86d5ImIu4/orVg9hw7AAmRCW7db3WNg//O28SzP66jU5A/b1yWxMmDGuwo1quICCNjwxgZG8Z9pw9i9vJMZi7eyq2fpPHgV6s5b3g34rt1otphcDgMNcZQ49j7cDif11/ucD6vna5xQI3D4dyWA1+n3jYH2wdAkL8v7f19aOfvSzt/H9oF+NDeOd0+wPm3bplz3YC92/j7Ht3tu96mpKKatc7indpy/YzsYnaXVdWtE93RKuIZ3y/CeeDvSO+otlfE40qHm0CbXBhrjNm234vXHNae2jh7rp1enXrRKcB9FaMbc0u47ZM07NsKmTw0hofOHkJYC2wJHNren7+N68Vfx8axcFMeMxdv5b1Fm+sqDZtKBHxEsNkEX5vUTfvYBJs459kEm81az6feMh/nNrZ62/narIN4UVkV2bvL2FNRQ2llNXsqa6isdhwimr38fIQgv3qJo34CCfClnV+9BLNfoglyrmcT6hLYvgnRmfAcNCm51b1GTb2kaept4wCHMVQ7X7N+oi0qr2LtzmK25e9bxNOvczCnDY1hYMzes/2Qdi3vd9jaNTUZbBORMYBx9kR6E7DGdWG1Lg7jwJ5jZ0LPCe7Zn8PwzsLNPP5dBgG+Pjx/4TDOTPDeri+aSkQY0zuCMb0j2F1aRX5ppXXQ9qk9QO97EK9/IK89gLtLVY2D0soayipr2FNZTWmF829ldV3SKK2sobSyhj0V1fv+dS7bWVxO6a59t3e4uTP3xhJo7edam0Db+fmS0C2EaUndrXJ9LeJpUZqaDK4FnsMaqWw7MBe43lVBtTZ/7v6TosoitxQRZRaUcsesdBZuyuOE/pE8dl58qxxzoFM7P6+uQPTzsdEpyNasrayNMVRUOw5MHBU1OIzZe+Wy/5XOfgmyNnH62mxelUCVZzUpGRhjdgEH9Eyqmqa2vmBY1DCX7cMYwycp23hozpp9uprWszIXKdwKvz4O6+dC1EDoNtL5SIL2rrlDS0QI9PMh0M+nRRb3Ke/WaDIQkReAg16YGmNubPaIWqHUnFRCA0Lp2bGnS16/KV1Nq2ZSkgPzn4KUtwCB/pOg4E/4/Rkwzmq00Lh9k0PnIeCrB2/l3Q51ZdCy7+f0Emm5aSREJbjkLP2rtB3c+4XV1fR9pw9qUlfT6giUFcKCF2DRK1BdDsMugePugk5dreWVpZBlh8yl1uPPebDiE2uZbyDEJFqJoTZJ1G6nlJdoNBkYY95xVyCtVX55PpuLNnN2n7Ob9XUL9lRy7xcrmZOedcRdTasmqCyFJf+F35+F8kIYch4cfzdE9Nl3Pf920HOM9QAwBoq2O5NDivV3yeuw8EVreYcu+yaHLong17L7zVEtW5PqDESkH3A7EMu+4xmc6JqwWo+0nDSgeesL6nc1ffvEfs3S1bTaT3UlpL4Lvz0JJdnQdyKceC/ENLHXdhHo1M16DD5n72vuXLE3OWQuhTVfWstsvlZxUv3ipbBe1uso5QZNvZtoFvAq8AbavuCwpOam4mvzZXDE4KN+reLyKh6as5pPUjIZEN2BGX8Z2exdTbd5jhpY8Sn8+ggUbIYeY2DKDOg5+uhf29cfuo6wHsnXWPNKcmF7veSQ9iEsfd1a1i58b2LoNhK6DIdA7ZJDuUZTk0G1MeYVl0bSSqXlpDEofBABPkc3SHxZZQ1TXl3Iup3FXHe8e7qablOMgbXfwM8PQ85qiB4KF38KfU527dl5cKRVCd1/kvXcUQO5GXuTQ2YKrPvOubI471yqV7wU0R9selWojt6h7iYKc05+JSLXAZ+xbxfWBx3IXkTeAk4HcowxQ5zzHgCuAnKdq91tjPnmiKP3cpU1lazctZILB1x41K/16LdryMgu5q0rkjhxgPd3J9GibPoNfvq3dYYe3gfOfxsGne2Zg6zNBzoPth4jrrDmlRXCjuV7i5fWfAXL37WWBXSErsMhapBVrBQWZ93NFNIDfLy3HYbyPoe6MliGdWtp7anRHfWWGaBXI9vOAF4E3t1v/jPGmOmHEWOLtSZ/DZWOShKjEo/qdX7JyOHdhVu48tg4TQTNafsyKwls+hU6doUzX4CEi8DHy7rMDgqB3idaD7CuYvI31bt6WArLZkBVvQ6FxQdCulsJIjRub6II6wWhsa2rsrqiBIqzrbqd4myoKgNHtXWrr8Ph/FvTwLxqa37tcuNo2rx9lh9kntggKAzahe39Wzcd7nwebj33ktuOD3U3URyAiAQaY8rrLxORRpu1GmPmiUjsUUfYgtU2NjuaZLCrpII7Pk1jQHQH7jy1f/ME1tblZMDPD0HGHOsf8pRHIOmv4NdCWmqLQHhv65FwgTXPGCjZCfl/Wokif5PV/iF/k5X0ynfv+xodujgTROyBCSPQS+qhKkv3HuCLs5x/G3heWXzk+7D5WonT5uOctlnTB53n65y21Vteb13fAOuvo9qKPWc1lOZD1Z6Dx+AfvF/SCN83aQSF1pt2zvdv/nZETT0FWgAMb8K8prhBRC7DasNwmzGm4Aheo0VIzUmlW3A3IoKOrEWqMYa7Pk2nqLya9/+WrN02H62CLfDro5D+Mfi1t24RHX0dBHTwdGRHTwQ6RFuPhiq7S/OdyaE2WTj/rv/BSiL1tQuvlxzqX1HEWa2rj7YOpbqigYN6vb8lO62/+ycwAJ8A5/uMsYrS+py093mHaAiOtg6U9Q/SDR24xce9xYBV5VCWb30PZflQmmdN1z13zivLt76b0nyoaOD91/INrJc0QutN75dMDsOh6gyisfojChKRYewtLuoIHElqegV4CKuI6SHgKeDKg+z7auBqgB49ehzBrjzLGIM9x86YLmOO+DU+WLyVnzJyuPf0QTqwy9Eo3gnzp0PK29aBYdR1MPZWaH94/ywtWm0xRdcRBy6rKLHunNr/imLrIlgxi306IfDv0MDVhDNhtIuAPbn7HtwPOLPPgrIGzv9sfnuTWURfiBu/9yAf3HnvwT4otGXebusXCH5doONhdBhZU2V9VvsnkLrpgr0JJHulc7qARjqNaNShrgxOAa4AugH1xzsuBu4+3J0ZY+pOQUTkdWBOI+u+BrwG1uA2h7svT8ssziSvPO+Ii4g25JTw8NerGdc3gr+MiW3W2NqMsgL443lY/Kp1Njr8Uhh/p7b+3V9AMEQPsR77q66w+mGqTRS1VxQ7V0HGN+CoOnCb+sTHecbe2UoePUbvPbDX/xsUqndF7c/HD4KjrEdTORxW48japPFgcpM3bUoL5HdE5DxjzOymR9QwEYkxxmQ5n54DrDza1/RW9lw7cGT1BZXVDm7+OJUgPx+mT0nQ7iUOV+UeKwH88ZxV1DDkfDjhbquMXR0e3wDrTD2i74HLHDWwO3NvoijNt26VrX+QbxduFcso97DZ9l4FHqam9lo6W0QmA4OBwHrz/32wbUTkQ+B4IEJEMoH7geNFJBHrOmYzcM1hR9xCpOakEuwXTJ+QPodeeT/P/LiOlduLePWSEa2y+2mXqa6E5e/Ab0/AnhzoewqcdK/VZkA1P5sPhPa0Hr1P8HQ06ig1tTuKV7HqCE7AaoV8PrCksW2MMQ3dXP/m4QbYUtlz7SREJmCTw7v0XbQpj1d/28i0pO6cOiTaRdG1Mo4aSP/EajVcuBV6HgvT3oMeozwdmVItRlPvJhpjjIkXkXRjzIMi8hTwrSsDa8mKKovYULCBiT0nHtZ2u8uquPVjOz3D2nHfGYNcFF0rYox1e+jPD1utdmMS4PRnoPdJLbOSUSkPamoyqB3UtFREugB5QIxrQmr5VuSuwGAOu77g3s9XsrO4gtl/H0P7AC9r+OQtjLHuSMlcatUJbF8G4X2t/oMGnqWVkEodoaYeceaISAjwBFarZLCKi1QDUnNSsYmN+Igm9nAJfJ66nS/TdnDbhH4kdg9xXXAticNh3eaYnQ5ZaZDl/Fu6y1resRuc+SIkXOh9rYaVamGa+h80Hfg7MA5YCMzHajOgGmDPtdM/tD/t/JrWFGNbfin3fr6SpJ6hXHfC4Vc4two11bBr3b4H/ux0qCiyltt8IXIg9DvV6kY6JgG6DLPudlFKHbWmJoN3sNoWPO98fhFWn0NTXRFUS1btqCY9N73Jg9nUOAy3fmLHAM9MS8SnLdxGWlVuNdPPStt78N+5yhpBDMA3yLrnfegU66AfE291xKYHfqVcpqnJYIgxpn6N5i8istoVAbV06wrWUVZdRmJkYpPWf+XXDSzdXMDTU5swbvH2ZZC77sDOrwI6eW9ZeUWx1Tqy/oE/N8PquwWs2GPirb6BYhKsR3gfLfZRys2a+h+3XERGGWMWAYhIMjo+coNqO6dryshmadsKefbH9ZweH8M5ww7RKjbja/j40r2DrtcnPs7OrMIO0l/Jfp1c1XZ+1dyNgUrznUU89Q78eRupax7fPtI62Pc7BaKdRT2hsXrnj1JeoKnJYASwQES2Op/3ANaKyArAGGOaXlPaytlz7ES1iyK6feNtBPZUVHPzx3aiOgTwn7OHIo0dEDf8CLOusA6eZ78ClSX7dmy1/3TBZqv/+9I8qKk8+OsGhuyXQGqnG+glsXba13/vHT21Fbq1B//d2/a+dqce1hl//LS9B/4O0XrgV8pLNTUZnOrSKFoRe66dYVHDGj+4Aw9/vZrNeXuY+bdRdGrXyCAkm/+Ajy6BiH5wyezDa2ZujNU1w6F6SSzNsw7uOaut6fr94u/Pv4NVhFPX2ZhYxTrdk+GYq6yDfnT8ETWHV0p5TlO7o9ji6kBag+w92WTtyeLywZc3ut73q7L5cMk2rjmuF6N7N9JzZmYKzJxqDVJy6eeHf4AVsTohCwi2Rr5qqrrudvP2SxrOXhKry63hF2MSrEHcA4IPLy6llNfRWrpmVNc5XSOVxzlF5fxzdjqDu3TktgmNDFaTlQ7vn2v1H3/ZF1YHYO5yJN3tKqVaNC+9BaVlsufYCfINol9YvwaXOxyG2z9Np7SyhucuSMTf9yAff04GvHe2VSRz2Zd6UFZKuZwmg2Zkz7EzJGIIfraG6wDeWbiZeetyuWfyQPpEHWR0rbyN8O5ZViOry7+0eoRUSikX02TQTEqrSsnIzzhoEdHa7GIe/TaDEwdEccmogxzgC7dZiaCm0ioa0v73lVJuonUGzWTlrpXUmJoGO6erqK7hpo9S6RDgy+PnxTd8p1FxNrx7JpQXWVcEUQNdH7RSSjlpMmgmtZXHCZEJByx78ru1ZGQX8+blSUR2aKBLhT27rCuC4p1w2efQJdGlsSql1P40GTST1JxUenfqTaeATvvM/339Lt74/U8uGdWDkwZ2PnDDsgKrsrhgM1w8C7of45Z4lVKqPq0zaAYO4yAtN+2AIqKCPZXcNstO78j2/Ou0BgarqSiG98+37h6a9gHEjXdPwEoptR+9MmgGmwo3UVxZvE9/RMYY7v5sBfl7Knnz8pEE+e/XD1BlKcy8AHakwtR3oO/Jbo5aKaX2ctmVgYi8JSI5IrKy3rwwEflBRNY7/4a6av/uVNfYrN6VwaxlmXy7MpvbJvZnSNd9i46oroCPL4Etf8A5/4WBZ7gvWKWUaoAri4lmcGCfRv8EfjLG9AV+cj5v8VJzUgkLDKNHB6vLhy15e3jwy1WM6hXGVeN67btyTRV8eiVs/AnOfB7ip3ggYqWU2pfLkoExZh6Qv9/ss7AGysH592xX7d+d0nLTSIhMQESoqnFw00d2fGzC01P3G6zGUQOfXWMN4j7pCRh+meeCVkqpetxdgdzZGJPlnM4GGri9pmXJK8tjS9GWuvqCF37egH1bIf85ZyhdQoL2ruhwwFc3wsrZcPIDkHyNZwJWSqkGeOxuImOMoW7UkwOJyNUikiIiKbm5uW6M7PCk5aYBVn3Bsi35vPjzes4d1pUzEur1J2QMfHcXpL4P4++Esbd4KFqllGqYu5PBThGJAXD+zTnYisaY14wxScaYpMhIN/bYeZjsOXb8bH50b9+Xmz+20yUkiAfPGrx3BWPgx/thyWsw+gY44W7PBauUUgfh7mTwJVDb2f/lwBdu3n+zS81JZVD4IB79eiPbC8p4dloiHQLrdVT32xPwx3OQdCVMfFhH+lJKeSVX3lr6IbAQ6C8imSLyV+AxYIKIrAdOdj5vsSprKlmVt4pO0pfZyzO5/oQ+JMXWG4BmwQvw6yOQcCGc9pQmAqWU13JZozNjzIUHWXSSq/bpbqvzVlPlqGL+ymASuodw40l99y5c8jrMvQcGnQ1nvgg2beytlPJeeoQ6Cqk7UwGoLu3Bs9MS8fNxfpypH8A3t0O/U+Hc160xg5VSyotpMjgKn2f8gaMynPsmJRMX0d6auXI2fHkD9DoeprwDvv4ejVEppZpCk8ERWrm9kI1Fq+js359pI7tbM9d+C/+7GronwwUzrbGElVKqBdBkcATKq2q4YdaPiG8Jlw0/wRqsZuPP8MllEB0PF30C/u09HaZSSjWZJoMj8Og3a8gsWw3Asd1GwOY/4MOLIKIfXDIbAjt6OEKllDo8mgwO0y9rc3hn4RYGxeXTwa8DvUsKYeZUCOkOl34O7cIO9RJKKeV19DaXw7CrpII7ZqXTv3MHJHAz8X69sH1wHrSPsAawD/beltJKKdUYvTJoImMM/5ydTlF5FY+c35tNuzcybPNS8A+Gy76Ejl0O/SJKKeWlNBk00cwlW/lxTQ53nTqA0pIFAAyrwkoEoT09G5xSSh0lTQZNsCGnmIfmrGZc3wj+MtgH+2//xscYhkz5ECL6eDo8pZQ6apoMDmF3aRV/eyeF4ABfnj4tBtt7Z2O3VdG/Yxztug73dHhKKdUsNBk0orrGwQ0fLmd7YRlvnB9H5P+mUl2czYqg9iR2HePp8JRSqtloMmjEY99mMH/9Lh47rQeJv/4FCv5k7RlPUOaorBvZTCmlWgNNBgcxe1kmb/z+J39L7sx5a2+HnDUw7X3sflY31IlRiZ4NUCmlmpEmgwakbi3g/z5bwdi4Ttxd+gRsXQTn/hf6TsCeYye6fTTR7aM9HaZSSjUbTQb72VlUzjXvLSO6gx9vhr6Dbf33MHk6DDkPAHuuncTIRM8GqZRSzUyTQT3lVTVc/d4ySiqq+LzPNwSs/gROuAdG/g2A7D3ZZO/J1iIipVSro91ROBljuPt/K0jbVsjcEUsJW/EmJP8dxt9et05qjjWYjVYeK6VaG70ycHpj/p/8L3U7bw5eQb9Vz0D8NDjlkX3GLbbn2AnyDaJfaD8PRqqUUs1PkwHw27pcHv12Df+KzeDEjY9B31PgrJcOGLc4NSeV+Ih4fG16QaWUal08kgxEZLOIrBARu4ikeCKGWptyS7hh5nKmhW3gbzmPIt2TYcoM8PHbZ73SqlLWFawjISrBM4EqpZQLefIU9wRjzC4P7p+i8ir+9m4KibaN/KfyMSSyP1z0Mfi3O2DdFbtWUGNqtL5AKdUqtdliohqH4cYPU/HLW89bvo9jC46yRikLCmlwfXuOHUGIj4x3b6BKKeUGnkoGBpgrIstE5OqGVhCRq0UkRURScnNzmz2AJ77PYN3aNfyv45P4+fnDpZ9Bh4M3JEvNTaV3SG86+uuQlkqp1sdTyWCsMWY4MAm4XkTG77+CMeY1Y0ySMSYpMrJ5RxD7PHU7s36z80Wn6bQ35XDp/yCs10HXdxgH6Tnp2r5AKdVqeSQZGGO2O//mAJ8Bx7hr3+mZhfx79mJmdXiKiJocq44gemij26zNX0txVbHWFyilWi23JwMRaS8iHWqngYnASnfsO6eonBveWchr/k/Tq3oTMvVd6Dm60W0WbF/AtT9eS5BvEMnRye4IUyml3M4TdxN1Bj4TqzGXLzDTGPOdq3daUV3Dde8t4d7Kp0iSFXDOa9DvlIOuX+2o5iX7S7yx4g36hPThyfFP0rl9Z1eHqZRSHuH2ZGCM2QS49WZ9Ywz3/G8F52c9xQTfpXDqY5Aw7aDrZ5Vkcee8O7Hn2jmv73ncdcxdBPkGuTFipZRyrzbRlPbtPzYTl/4UF/j+CuPvgFF/P+i6v2z9hXv+uIdqRzWPj3uc03qd5r5AlVLKQ1p9Mvh9/S52fvck/+f7JWbElcgJ/2pwvcqaSp5Z9gzvr3mfgWEDmX7cdHp07OHmaJVSyjNadTLYvGsPcz94in/7fkDVwLPxmzx9n47nam0r2sbt825ndd5qLh54MbeOuBV/H38PRKyUUp7RapNBcXkVb735IvfzCmU9jiPovNfB5nPAet/9+R0PLHwAm9h49oRnOanHSR6IVimlPKtVJgOHw/DKjHf4V+mTlEYm0OHimeC775l+eXU5jy99nE/XfUpCZAJPjH+CLsFdPBSxUkp5VqtMBu9//hV/z7qHsuDuhFz5GQQE77N8Y+FGbv/tdjYUbuCvQ/7K9cOux8/md5BXU0qp1q/VJYNf/viD09Kup9q/IyFXfwXtwuqWGWP4YuMXPLL4EYJ8g3jl5FcY23WsB6NVSinv0KqSQca6tfSbexl+NiHob18hnbrVLdtTtYeHFz3MnE1zOCb6GB4d9yhR7aI8GK1SSnmPVpMM8nKz8f/wPEJkD5UXf4l/5/51yzLyM7jjtzvYWryV6xKv4+qhV+PTQGWyUkq1Va0iGVSWFpP32lnEOrLInPw+vfpY/d4ZY/h47cc8ufRJQgJCeGPiG4yMHunhaJVSyvu0+GRgqivY/PK59K5cy7Lk5zjmmEkAFFUW8cCCB/hhyw+M7TqW/4z9D2GBYYd4NaWUaptadjJw1LDljUvpV7KEb3r/i9NOuxyA9Nx07px3Jzv37OTWEbdy+eDLsUmbHdRNKaUOqeUmA2PI/uhGYrO/55PQqzjvkjtwGAfvrnqX55Y/R1S7KGZMmkFCpA5gr5RSh9Jik8Hubx8iet37fOR/LpOveZSiykL+9fu/mL99Pif3OJkHxjxAp4BOng5TKaVahBaZDCr+eJlOS57if5xA8lUvsLYwjbvm3UVBRQF3J9/NBf0vQBrog0gppVTDWlwycKR9TMAP/8f3NUmEXfQi3+94n1fSXqF7h+68eNKLDAwf6OkQlVKqxWlZyWDdXMznf2dhzSDWHfcQaVsfYHH2Yib3msy9o+6lvV97T0eolFItUstJBlsXUfPxpayu6c6bA/7Kurx/UlZVxr/H/Juz+5ytxUJKKXUUWkYyqCqj5v2pbKoO5ZZu49lZ/Rx9gvsw/ZTp9A7p7enolFKqxfPIzfcicqqIrBWRDSLyz0NukLeR1TV+XNg1lp0Bv3Je3/OYOXmmJgKllGombr8yEBEf4CVgApAJLBWRL40xqw+2zW4MF3cNJcC/gCeOfYJJcZPcFa5SSrUJnrgyOAbYYIzZZIypBD4Czmpsg0w/G9HB3Zl95ixNBEop5QKeSAZdgW31nmc65+1DRK4WkRQRSfGtac+c8z7WAeqVUspFvLbDHmPMa8aYJGNMUv+oWB2gXimlXMgTyWA70L3e827OeUoppTzEE8lgKdBXROJExB+4APjSA3EopZRycvvdRMaYahG5Afge8AHeMsascnccSiml9vJIozNjzDfAN57Yt1JKqQN5bQWyUkop99FkoJRSSpOBUkopTQZKKaUAMcZ4OoZDEpFiYK2Hw4gAdnk4BvCOOLwhBvCOOLwhBvCOOLwhBvCOOLwhBoD+xpgOTVmxZXRhDWuNMUmeDEBEUjwdg7fE4Q0xeEsc3hCDt8ThDTF4SxzeEENtHE1dV4uJlFJKaTJQSinVcpLBa54OAO+IAbwjDm+IAbwjDm+IAbwjDm+IAbwjDm+IAQ4jjhZRgayUUsq1WsqVgVJKKRfSZKCUUkqTgVJKKS9PBiISJiJhno5DeR8RGe7pGJT3EZEIT8fQUnldMhCRHiLykYjkAouBJSKS45wX6+Hw2hwRGSAi34rI1yLSW0RmiEihiCwRkYFuimH4fo8RwJciMqytJwUR6SgiI0Qk1NOxuJuITBKRP0Xkd+dvYRWwWEQyReQkD8UUKiIdPbHvo2aM8aoHsBCYBvjUm+eDNSLaIi+Ib4Ub99Ud+AiYD9wN+NVb9rmbYpgHnAFcCGxxfg/inPeTm2JwAAuAX+o9ypx/f3bj93FlveluwE9AoTO2fm6K4X0gwjl9CrAV+NH53Uxx42eRD7wBnITzrkR3PwA7MBAYDeQBo5zzBwLL3RhHF+BdYDdQ4/xOtgIP1P+fdWM8nYHhzkfnJm/niS/xEG9k/ZEsa+YYzj3I4zwg142fxQ/AtUAi8ILzoBPuXJbqphhS601v2G+ZW/7hnJ/7b8CkevP+dNf30ND7BT4Brsa6uj7HjYlxRb3pBUCsczoCSHPjZ7EWuAH4A2sM8+dqD8Ye+j627bfM7sY4fgaOd06fCzwDtAceBl5zYxyJwCJgjfME4Ucgwzlv+KG298a+iZaJyMvAO8A257zuwOVAqpti+Bj4AGioEUagm2IAiDTGvOqc/oeIXALME5EzaTg2V/CpN/30fsv83RGAMWa2iHwPPCQiVwK34b73fzD9jDFTndOfich9btqvTUQ6GmOKsK6YtgIYY3aJiDv/n/cYY14EXhSRHlhXjC+LSAjwkTHmbjfEUCgi1wAdgQIRuQUrSZ8MlLhh/7XCjTG/Ahhj/ici/zLG7AHuEZEMN8YxA7jGGLO4/kwRGQW8DSQ0trE3JoPLgL8CDwJdnfMyga+AN90UQzow3Rizcv8FInKym2IA8BORQGNMOYAx5n0RycYaP7q9m2J4SUSCjTElxpiXa2eKSB+sMw+3MMaUALeIyDCsE4Vgd+27nm4i8jxWMVmkiPgZY6qcy/zcFMODwC8i8hLWWfksEfkSOAH4zk0xgPUZAGCM2Qo8ATwhIgOwinnd4XLgHqykOBGrKPN7rCKzq9wUA0Cu80TtF6wrg80AIiK4t162/f6JAMAYs0hEDnm80BbIDRCRccAW5498/2VJxpgm9wR4lHHcgnUp/Nt+84cBTxhjJrgjDm/j/Cfr4Dw7dud+L99v1pfGmAIRiQZudNPZcG0ivgroh3VCl4lVh/S9O/bvjOFpY8yt7tqfN3NeGU0HBmHVY9xhjMkSkXCs4qPZborjeaA3Vv1F/VKVy7CKVW9odPuWlAxE5HRjzBxPx6Es3vB9eEMMyvu01d+FiEwCzmJvqcp2rJOWbw61rdfdWnoIIz0dgIic7ukYwGvi8Pj3gXfE4BXfhzfEAF4TR5v8XRhjvjXGXGuMOcP5uLYpiQC8s84AZ7ljQ9ntfs9FVWck4A1nHG6Lwxu+D2+I4RC84XfhDTFAG/ttHoJXfCcicrUxptEeTL2umEhE7sKqCPoIqywUrHu6L8C6S+ExN8VxsB/ZGnfs31vi8IbvwxtiqBeLx38X3hCDN8Shv4umE5FrjDH/bXQdL0wG64DB9e7SqJ3vD6wyxvR1Qwxe8SPzhji85PvweAzO/XnD9+HxGLwlDv1dNJ2I/MUY83ajK7mrQcRhNJzIAHo2ML8n1ljI7ohhHQ20HMS6r94tDd+8JQ4v+T48HoMXfR8ej8Fb4tDfxWHFuPVQ63hjncHNwE8isp69t0f1APpgtXh0BwdWE/Mt+82PcS5zF2+I42Y8/314QwzgHd+HN8TgLXHcjP4u6ohI+sEWYXVR0SivSwbGmO9EpB9wDPuWvy01xtS4KYyb8Y4fmcfj8IbvwxticLoZz/8uvCEGr4hDfxcH6IzVX1XBfvMFq+uSRnldnYG3EBEbnv+ReU0cyuIN34c3xOBNcXgDb/gsRORN4G1jzO8NLJtpjLmo0e01GSillGppjc6UUkq5gCYDpZRSmgyUdxJrRLXzPR2HNxGRm0WknafjUK2TJgOlXKiZxxi4GdBkoFxCk4HyOBG5TETSRSRNRN6rt2i8iCwQkU21VwkiEiwiP4nIchFZISJnOefHisgaEXldRFaJyFwRCXIuG+l8fbuIPCkiK53zfZzPlzqXX9NAbLEikiEiHzhf/9Pas3Oxxh7+TUSWicj3IhLjnP+riDwrIinATc79L3C+vyUi0uFg+xaR453bf1pvvyIiN2Ldy/6LiPziXPcVEUlxvt8H68V8mnPbZSLyvIjMcc5vLyJvOWNIrf3slAK8rwWyPtrWAxiM1YKzdlzfMOffGcAsrBOWQTiH3MRqG9PROR0BbMC6jzoWqAYSncs+AS5xTq8ERjunHwNWOqevBu5xTgcAKUDcfvHFYo2qdqzz+VvA7ViD2SzAGo0OrAFd3nJO/wq87Jz2BzYBI53POzrfQ4P7Bo7HGku3m/O9LwTGOtfbXPs57fdZ+Tj3GY81Et+22vcBfAjMcU4/Uu8zCXF+7u09/RvQh3c8vK7RmWpzTgRmGWN2ARhj8ust+9wY4wBWi0htC0oBHhGR8VitO7uyt3Xln8YYu3N6GRAr1jCMHYwxC53zZwK13QpPBOLr1U10AvoCf+4X4zZjzB/O6feBG7FGFRsC/CAiYB2Qs+pt87Hzb38gyxiz1Pn+igBE5GD7rgSWGGMynevZsRLSAfeOA1NF5Gqs5BKDlTRtwCZjTO17+BAr8dS+3zNF5Hbn80CsxlFe0Zma8ixNBsqbVdSbrh1m8WIgEhhhjKkSkc3sHZe6/vo1QNAhXl+Af5hDjxC2f2Mc49x2lTFm9EG22XMk+xaR4znwfRzwfyoicVhXKCONNdraDA49PrcA5xlj1h5iPdUGaZ2B8rSfgSliDRGIiIQdYv1OQI4zEZyA1SnZQRljCoFiEUl2zrqg3uLvgb+LiJ9z3/2k4bFie4hI7UH/Iqyz9LVY4yCPdm7rJyKDG9h2LRAjIiOd63VwVio3dd/1FQMdnNMdsRLObudV06R6++slIrHO5/XHI/4e+Ic4L2XEGj5VKUCvDJSHGWNWich/gN9EpAZIBa5oZJMPgK9EZAVWOXtGE3bzV+B1EXEAv2GVyQO8gVUEs9x5gMwFzm5g+7XA9SLyFrAaeMUYU+ks4nleRDph/S89C6za7/1Visg04AVnhXYZcPJh7Lu+14DvRGSHMeYEEUl1vv9twB/O/ZWJyHXO9fYAS+tt/5AzxnSxuk/4k71FZqqN0+4oVKsnIsHGmBLn9D+BGGPMTU3cNharAnaIC0NsVrXv15lkXsLqRvkZT8elvJsWE6m2YLLzttKVwDjgYU8H5GJXOSueV2EVqzU6wpVSoFcGSiml0CsDpZRSaDJQSimFJgOllFJoMlBKKYUmA6WUUsD/A2kMd6y79TnlAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "import os\n",
    "def get_data(results, name):\n",
    "    output = {}\n",
    "    print(results, name)\n",
    "    for n in results:\n",
    "        output[n] = results[n][name]\n",
    "    return output\n",
    "\n",
    "def getMeanSTD(inputarray):\n",
    "    mean = []\n",
    "    std = []\n",
    "    for i in range(len(inputarray)):\n",
    "        mean.append(np.mean(inputarray[i]))\n",
    "        std.append(np.std(inputarray[i]))\n",
    "    return np.array(mean), np.array(std)\n",
    "\n",
    "def plt_dict(p_dict, y_title, file_name):\n",
    "    plt.figure()\n",
    "    names = []\n",
    "    for t_name in [\"narrow\", \"turtle\", \"wide\"]:\n",
    "        name = p_name + \"_\" + t_name\n",
    "        print(name, p_dict)\n",
    "        mean, std = getMeanSTD(p_dict[name])\n",
    "        plt.plot(np.array(ch_perc_array), mean)\n",
    "        plt.fill_between(np.array(ch_perc_array), mean - std, mean + std, alpha=0.2)\n",
    "        names.append(name.split(\"_\")[1])\n",
    "    plt.legend(names, loc='upper left')\n",
    "    plt.xlim(0.0,1.0)\n",
    "    plt.xticks(np.array(ch_perc_array), rotation=90)\n",
    "    plt.xlabel('change percentage')\n",
    "    plt.ylabel(y_title)\n",
    "    if not os.path.exists('graphs'):\n",
    "        os.makedirs('graphs')\n",
    "\n",
    "    plt.savefig('graphs/' + file_name + \".pdf\", bbox_inches='tight')\n",
    "\n",
    "for n in lambdas[p_name]:\n",
    "    if n == \"has_sol\":\n",
    "        continue\n",
    "    plt_dict(get_data(result, n), n, '{}_{}'.format(p_name, n))\n",
    "plt_dict(get_data(result, 'hdiversity'), 'Hamming Diversity', '{}_{}'.format(p_name,'hammingdiversity'))\n",
    "# plt_dict(get_data(result, 'cdiversity'), 'compression diversity', '{}_{}_{}'.format(p_name,exp_name,'compressiondiversity'))\n",
    "# plt_dict(get_data(result, 'pdiversity'), 'pca diversity', '{}_{}_{}'.format(p_name,exp_name,'pcadiversity'))\n",
    "# plt_dict(get_data(result, 'sdiversity'), 'svd diversity', '{}_{}_{}'.format(p_name,exp_name,'svddiversity'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing solid probability with 0.5 change rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Error: the file zelda_narrow_noaction_heatmap_changes_100M could not be found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13968\\1136006250.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     49\u001b[0m     \u001b[0mregions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[0mboth_perc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPPO2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0msolid_perc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.01\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Testing {} at solid percentage of {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msolid_perc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(cls, load_path, env, custom_objects, **kwargs)\u001b[0m\n\u001b[0;32m    873\u001b[0m         \u001b[1;33m:\u001b[0m\u001b[0mparam\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mextra\u001b[0m \u001b[0marguments\u001b[0m \u001b[0mto\u001b[0m \u001b[0mchange\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mloading\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m         \"\"\"\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_from_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'policy_kwargs'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36m_load_from_file\u001b[1;34m(load_path, load_data, custom_objects)\u001b[0m\n\u001b[0;32m    606\u001b[0m                     \u001b[0mload_path\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;34m\".zip\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    607\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 608\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Error: the file {} could not be found\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    610\u001b[0m         \u001b[1;31m# Open the zip archive and load data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error: the file zelda_narrow_noaction_heatmap_changes_100M could not be found"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "from stable_baselines.common.policies import MlpPolicy, CnnPolicy, FeedForwardPolicy\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "from stable_baselines.a2c.utils import conv, linear, conv_to_fc\n",
    "from stable_baselines.results_plotter import load_results, ts2xy\n",
    "from stable_baselines import PPO2\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "import pdb\n",
    "\n",
    "p_name = \"zelda\"\n",
    "rep_names = [\"narrow\", \"turtle\"]\n",
    "exp_names = [\"noaction_heatmap_changes_100M\", \"heatmap_changes_100M\"]\n",
    "\n",
    "def get_env_func(p_name, r_name, solid_perc=0.2):\n",
    "    def get_env():\n",
    "        env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), 28, random_tile=False, change_percentage=0.4, probs={\"empty\":1-solid_perc, \"solid\":solid_perc})\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "def get_action(env, model, action_type=True):\n",
    "    action = None\n",
    "    if action_type == 0:\n",
    "        action, _ = model.predict(obs)\n",
    "    elif action_type == 1:\n",
    "        action_prob = model.action_probability(obs)[0]\n",
    "        action = np.random.choice(a=list(range(len(action_prob))), size=1, p=action_prob)\n",
    "    else:\n",
    "        action = np.array([env.action_space.sample()])\n",
    "    return action\n",
    "\n",
    "pathlength_perc = {}\n",
    "pathlength={}\n",
    "regions_perc = {}\n",
    "regions={}\n",
    "both_perc = {}\n",
    "for i in range(len(exp_names)):\n",
    "    r_name = rep_names[i]\n",
    "    e_name = exp_names[i]\n",
    "    m_name = \"{}_{}_{}\".format(p_name, r_name, e_name)\n",
    "    pathlength_perc[m_name] = []\n",
    "    pathlength[m_name] = []\n",
    "    regions_perc[m_name] = []\n",
    "    regions[m_name] = []\n",
    "    both_perc[m_name] = []\n",
    "    model = PPO2.load(m_name)\n",
    "    for solid_perc in np.arange(0.0, 1.01, 0.05):\n",
    "        print(\"Testing {} at solid percentage of {}\".format(m_name,solid_perc))\n",
    "        temp_path = []\n",
    "        temp_length = []\n",
    "        temp_regions = []\n",
    "        temp_number = []\n",
    "        temp_both = []\n",
    "        for i in range(20):\n",
    "            env = DummyVecEnv([get_env_func(p_name, r_name, ch_perc)])\n",
    "            done = False\n",
    "            obs = env.reset()\n",
    "            while not done:\n",
    "                action = get_action(env, model, 0)\n",
    "                obs, rewards, done, info = env.step(action)\n",
    "            temp_path.append([0,1][info[0]['path-length']>=48])\n",
    "            temp_length.append(info[0]['path-length'])\n",
    "            temp_regions.append([0,1][info[0]['regions']==1])\n",
    "            temp_number.append(info[0]['regions'])\n",
    "            temp_both.append([0,1][temp_path[-1] == 1 and temp_regions[-1] == 1])\n",
    "        pathlength_perc[m_name].append(np.mean(temp_path))\n",
    "        pathlength[m_name].append(np.mean(temp_length))\n",
    "        regions_perc[m_name].append(np.mean(temp_regions))\n",
    "        regions[m_name].append(np.mean(temp_number))\n",
    "        both_perc[m_name].append(np.mean(temp_both))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (21,) and (0,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3900\\2121413545.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mnames\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mregions_perc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1.01\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mregions_perc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mnames\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2840\u001b[0m     return gca().plot(\n\u001b[0;32m   2841\u001b[0m         \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2842\u001b[1;33m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2843\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2844\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1741\u001b[0m         \"\"\"\n\u001b[0;32m   1742\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1743\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1744\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1745\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    271\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    272\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 273\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    274\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    397\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[0;32m    400\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[0;32m    401\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (21,) and (0,)"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in regions_perc:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),regions_perc[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Region == 1')\n",
    "plt.savefig('const_regions.pdf')\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in pathlength_perc:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),pathlength_perc[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Path >= 48')\n",
    "plt.savefig('const_path.pdf')\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in pathlength:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),pathlength[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Path Length')\n",
    "plt.savefig('pathlength.pdf')\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in regions:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),regions[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Number of Regions')\n",
    "plt.savefig('regions.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = wrappers.CroppedImagePCGRLWrapper(\"zelda-turtle-v0\", 22, random_tile=False, change_percentage=0.2)\n",
    "env.reset().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2280549686.py, line 15)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\Maryam\\AppData\\Local\\Temp\\ipykernel_3900\\2280549686.py\"\u001b[1;36m, line \u001b[1;32m15\u001b[0m\n\u001b[1;33m    'hamming': lambda lvl1, lvl2: return np.clip(abs(lvl1 - lvl2), 0, 1).sum()\u001b[0m\n\u001b[1;37m                                       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "def get_tpkldiv_diversity(lvls):\n",
    "    hamming = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_hamming = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                diff = np.clip(abs(lvl1 - lvl2), 0, 1)\n",
    "                lvl_hamming.append(diff.sum())\n",
    "        hamming.append(np.mean(lvl_hamming) / (lvls[0].shape[0] * lvls[0].shape[1]))\n",
    "    return hamming\n",
    "\n",
    "diversity={\n",
    "    'hamming': lambda lvl1, lvl2: return np.clip(abs(lvl1 - lvl2), 0, 1).sum()\n",
    "    'tpkldiv': lambda lvl1, lvl2: return calc_tp_kldiv(lvl1, lvl2, 3)\n",
    "    ''\n",
    "}\n",
    "\n",
    "def get_diversity(lvls, d_type):\n",
    "    result = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_hamming = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                if d_type == \"hamming\":\n",
    "                    \n",
    "        result.append(np.mean(lvl_hamming) / (lvls[0].shape[0] * lvls[0].shape[1]))\n",
    "    return result\n",
    "\n",
    "def get_hamming_diversity(lvls):\n",
    "    hamming = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_hamming = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                diff = np.clip(abs(lvl1 - lvl2), 0, 1)\n",
    "                lvl_hamming.append(diff.sum())\n",
    "        hamming.append(np.mean(lvl_hamming) / (lvls[0].shape[0] * lvls[0].shape[1]))\n",
    "    return hamming\n",
    "\n",
    "env_func = get_env_func(\"binary\", \"wide\", 1, 28)\n",
    "lvls = []\n",
    "for i in range(5):\n",
    "    env = DummyVecEnv([env_func])\n",
    "    done = False\n",
    "    obs = env.reset()\n",
    "    while not done:\n",
    "        action, _ = model.predict(obs)\n",
    "        obs, rewards, done, info = env.step(action)\n",
    "    lvls.append(env.get_attr('pcgrl_env')[0]._rep.get_observation()['map'])\n",
    "diversity = get_hamming_diversity(lvls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lvls' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3900\\3401017067.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mcomp_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mcomp_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mcomp_both_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'lvls' is not defined"
     ]
    }
   ],
   "source": [
    "import zlib\n",
    "\n",
    "\n",
    "comp_1 = zlib.compress(lvls[0].tobytes(), level=9)\n",
    "comp_2 = zlib.compress(lvls[1].tobytes(), level=9)\n",
    "comp_both_1 = zlib.compress(lvls[0].tobytes() + lvls[1].tobytes(), level=9)\n",
    "result_1 = (len(comp_both_1) - min(len(comp_1), len(comp_2))) / max(len(comp_1), len(comp_2))\n",
    "comp_both_2 = zlib.compress(lvls[1].tobytes() + lvls[0].tobytes(), level=9)\n",
    "result_2 = (len(comp_both_2) - min(len(comp_1), len(comp_2))) / max(len(comp_1), len(comp_2))\n",
    "print((result_1 + result_2)/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_narrow_noaction_heatmap_changes_100M\n",
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      "  [1.]]], (28, 28, 1), float32)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Error: the file tests/binary_narrow_noaction_heatmap_changes_100M.pkl could not be found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3900\\138669907.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_env_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mch_perc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPPO2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tests/{}.{}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me_format\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m \u001b[0menv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDummyVecEnv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mget_env_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mch_perc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[0mobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(cls, load_path, env, custom_objects, **kwargs)\u001b[0m\n\u001b[0;32m    873\u001b[0m         \u001b[1;33m:\u001b[0m\u001b[0mparam\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mextra\u001b[0m \u001b[0marguments\u001b[0m \u001b[0mto\u001b[0m \u001b[0mchange\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mloading\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m         \"\"\"\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_from_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'policy_kwargs'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36m_load_from_file\u001b[1;34m(load_path, load_data, custom_objects)\u001b[0m\n\u001b[0;32m    606\u001b[0m                     \u001b[0mload_path\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;34m\".zip\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    607\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 608\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Error: the file {} could not be found\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    610\u001b[0m         \u001b[1;31m# Open the zip archive and load data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error: the file tests/binary_narrow_noaction_heatmap_changes_100M.pkl could not be found"
     ]
    }
   ],
   "source": [
    "def get_action(env, model, action_type=True):\n",
    "    action = None\n",
    "    if action_type == 0:\n",
    "        action, _ = model.predict(obs)\n",
    "    elif action_type == 1:\n",
    "        action_prob = model.action_probability(obs)[0]\n",
    "        action = np.random.choice(a=list(range(len(action_prob))), size=1, p=action_prob)\n",
    "    else:\n",
    "        action = np.array([env.action_space.sample()])\n",
    "    return action\n",
    "\n",
    "def get_env_func(p_name, r_name, ch_perc=0.2, crop_size=28):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, add_visits=False, add_heatmap=True, add_changes=True, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), add_visits=False, add_heatmap=True, add_changes=True, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "print(m_name)\n",
    "print(p_name)\n",
    "print(r_name)\n",
    "print(get_env_func(p_name, r_name, ch_perc)().observation_space)\n",
    "model = PPO2.load('tests/{}.{}'.format(m_name, e_format))\n",
    "env = DummyVecEnv([get_env_func(p_name, r_name, ch_perc)])\n",
    "obs = env.reset()\n",
    "print(env.observation_space)\n",
    "action = get_action(env, model, 0)\n",
    "obs, rewards, done, info = env.step(action)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Box([[[0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]]\n",
       "\n",
       " [[0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]]\n",
       "\n",
       " [[0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]]\n",
       "\n",
       " [[0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]]\n",
       "\n",
       " [[0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]]], [[[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]], (5, 5, 5), float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_env_func(p_name, r_name)().observation_space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'zelda_narrow': {'player': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'key': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'door': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'regions': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'pathlength': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'iterations': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'changes': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'player_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'key_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'door_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'regions_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'has_sol': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'success': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]},\n",
       " 'zelda_turtle': {'player': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'key': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'door': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'regions': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'pathlength': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'iterations': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'changes': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'player_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'key_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'door_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'regions_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'has_sol': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'success': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]},\n",
       " 'zelda_wide': {'player': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'key': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'door': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'regions': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'nearestenemy': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'pathlength': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'iterations': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'changes': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'player_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'key_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'door_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'regions_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'nearestenemy_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'pathlength_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'has_sol': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'success': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 2],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 1, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_start = [\n",
    "       [0, 4, 0, 1, 0],\n",
    "       [2, 1, 1, 1, 1],\n",
    "       [0, 1, 1, 1, 0],\n",
    "       [0, 0, 1, 0, 0],\n",
    "       [1, 2, 0, 0, 0]]\n",
    "\n",
    "narrow_temp = [\n",
    "       [0, 4, 0, 4, 0],\n",
    "       [2, 3, 1, 1, 1],\n",
    "       [0, 0, 0, 0, 0],\n",
    "       [0, 3, 1, 0, 0],\n",
    "       [1, 0, 0, 0, 0]]\n",
    "\n",
    "turtle_temp = [\n",
    "       [0, 4, 0, 0, 0],\n",
    "       [0, 1, 3, 1, 0],\n",
    "       [0, 0, 4, 3, 0],\n",
    "       [0, 0, 1, 0, 0],\n",
    "       [1, 2, 0, 0, 0]]\n",
    "\n",
    "wide_temp = [\n",
    "       [0, 0, 0, 0, 0],\n",
    "       [4, 1, 1, 2, 1],\n",
    "       [0, 0, 0, 3, 0],\n",
    "       [0, 0, 1, 0, 0],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "second_start = [\n",
    "       [1, 0, 0, 3, 3],\n",
    "       [0, 0, 0, 1, 1],\n",
    "       [1, 0, 3, 0, 0],\n",
    "       [1, 0, 1, 0, 1],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "narrow_last = [\n",
    "       [1, 0, 0, 0, 0],\n",
    "       [0, 0, 0, 2, 1],\n",
    "       [0, 0, 3, 0, 0],\n",
    "       [1, 0, 1, 0, 1],\n",
    "       [4, 0, 0, 0, 0]]\n",
    "\n",
    "turtle_last = [\n",
    "       [1, 0, 0, 3, 4],\n",
    "       [0, 0, 0, 2, 1],\n",
    "       [1, 0, 3, 0, 0],\n",
    "       [1, 0, 1, 0, 1],\n",
    "       [0, 4, 0, 0, 0]]\n",
    "\n",
    "wide_last = [\n",
    "       [0, 0, 0, 0, 0],\n",
    "       [0, 0, 4, 1, 1],\n",
    "       [1, 0, 0, 0, 2],\n",
    "       [1, 3, 1, 0, 1],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "third_start = [\n",
    "       [4, 0, 1, 0, 0],\n",
    "       [1, 0, 2, 2, 0],\n",
    "       [1, 1, 0, 4, 0],\n",
    "       [0, 1, 1, 1, 0],\n",
    "       [0, 1, 0, 0, 1]]\n",
    "\n",
    "narrow_last = [\n",
    "       [4, 0, 0, 0, 0],\n",
    "       [1, 0, 0, 2, 0],\n",
    "       [1, 3, 0, 4, 0],\n",
    "       [0, 3, 0, 1, 0],\n",
    "       [0, 0, 0, 0, 1]]\n",
    "\n",
    "turtle_last = [\n",
    "       [4, 0, 0, 0, 0],\n",
    "       [1, 0, 2, 3, 0],\n",
    "       [0, 3, 0, 4, 0],\n",
    "       [0, 1, 1, 1, 0],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "wide_last = [\n",
    "       [4, 0, 0, 0, 0],\n",
    "       [1, 0, 2, 0, 0],\n",
    "       [1, 1, 0, 3, 0],\n",
    "       [0, 1, 0, 1, 0],\n",
    "       [0, 0, 0, 0, 0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 2],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info[0]['terminal_observation'].argmax(axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.525"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(result['sokoban_wide']['sollength'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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