Q-learning.ipynb

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   "source": [
    "# CE-40719: Deep Learning\n",
    "## HW6 - Deep Reinforcement Learning\n",
    "\n",
    "(30 points)\n",
    "\n",
    "#### Name: Sadroddin Barikbin\n",
    "#### Student No.: 98208824\n"
   ]
  },
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   "source": [
    "In this assignment we are going to design an agent to play atari game. We use a Deep Q-Network model as agent to play game Pong(`PongNoFrameskip-v4` environment of [gym](https://gym.openai.com/) library).\n",
    "In this environment, the observation is an RGB image of the screen, which is an array of shape (210, 160, 3). And action space is 6.\n",
    "We will train the model for 200,000 steps and should take approximately 2 hour. \n",
    "**If you train the model for 800,000 steps and achieve a score more than 10 in average in your last 10 games, you get a bonus point.**\n",
    "\n",
    "![Pong](https://cdn-images-1.medium.com/max/800/1*UHYJE7lF8IDZS_U5SsAFUQ.gif)\n"
   ]
  },
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   "source": [
    " ## 1. Setup"
   ]
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   "source": [
    "First, we need to install `stable-baselines`. This library  is a set of improved implementations of Reinforcement Learning (RL) algorithms based on OpenAI Baselines. We will use some of **wrappers** of this library. Wrappers will allow us to add functionality to environments, such as modifying observations and rewards to be fed to our agent. It is common in reinforcement learning to preprocess observations in order to make them more easy to learn from. \n",
    "\n",
    "- For linux based Operating Systems or google colab run cell below:"
   ]
  },
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     "timestamp": 1592042709002,
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      "displayName": "sadra barikbin",
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   ],
   "source": [
    "%%shell\n",
    "\n",
    "sudo apt-get update && sudo apt-get install cmake libopenmpi-dev zlib1g-dev\n",
    "\n",
    "pip install stable-baselines[mpi]==2.8.0"
   ]
  },
  {
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   "metadata": {
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    "id": "rCk0wAHtIvKX"
   },
   "source": [
    "- For Windows: \n",
    "    - First install [MPI for Windows](https://www.microsoft.com/en-us/download/details.aspx?id=57467) (you need to download and install `msmpisetup.exe`)\n",
    "    - Then run this command in Prompt: `pip install stable-baselines[mpi]==2.8.0`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Sx1Q4VpmM9qj"
   },
   "source": [
    "### 1.1. Import Libraries:"
   ]
  },
  {
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   "source": [
    "import random, pickle, os.path, math, glob, csv\n",
    "import numpy as np\n",
    "\n",
    "from timeit import default_timer as timer\n",
    "from datetime import timedelta\n",
    "\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "from IPython.display import clear_output\n",
    "from plot import plot_all_data \n",
    "\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# ######################## #\n",
    "# USE ONLY IN GOOGLE COLAB #\n",
    "%tensorflow_version 1.x \n",
    "# ######################## #\n",
    "\n",
    "import gym\n",
    "from gym.spaces.box import Box\n",
    "from stable_baselines import bench\n",
    "from stable_baselines.common.atari_wrappers import make_atari, wrap_deepmind\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "B08Nd1BBPTwS"
   },
   "source": [
    " ### 1.2. Hyperparameters"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Epsilon variables for epsilon-greedy:\n",
    "epsilon_start    = 1.0\n",
    "epsilon_final    = 0.01\n",
    "epsilon_decay    = 30000\n",
    "epsilon_by_frame = lambda frame_idx: epsilon_final + (epsilon_start - epsilon_final) * math.exp(-1. * frame_idx / epsilon_decay) \n",
    "\n",
    "# Misc agent variables\n",
    "GAMMA = 0.99 \n",
    "LR    = 5e-5\n",
    "\n",
    "# Memory\n",
    "TARGET_NET_UPDATE_FREQ = 1000 \n",
    "EXP_REPLAY_SIZE        = 100000 \n",
    "BATCH_SIZE             = 256\n",
    "\n",
    "# Learning control variables\n",
    "LEARN_START = 10000\n",
    "MAX_FRAMES  = 800000 # Probably takes about an hour training. You can increase it if you have time!\n",
    "UPDATE_FREQ = 1 \n",
    "\n",
    "# Data logging parameters\n",
    "ACTION_SELECTION_COUNT_FREQUENCY = 1000 \n"
   ]
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    "id": "_LhYJ6qyPTwY"
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   "source": [
    "## 2. Model (22 Points)\n",
    "We will implement a DQN model with experience replay. We implement a class for experience replay  `ExperienceReplayMemory`, for extarct features of observed picture of game we implement a class `DQN` that uses a CNN and inheritance from `nn.Module`. We implemented a wrapper class `WrapPyTorch` that you will use it in training loop."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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    "id": "fHB1ZPIqNUIw"
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   "source": [
    "### 2.1. Replay Memory (3 Points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "#################################################################################\n",
    "#                          COMPLETE THE FOLLOWING SECTION                       #\n",
    "#################################################################################\n",
    "# complete push and sample methods.\n",
    "#################################################################################\n",
    "class ExperienceReplayMemory:\n",
    "    def __init__(self, capacity):\n",
    "        self.capacity = capacity\n",
    "        self.memory = []\n",
    "\n",
    "    def push(self, transition):\n",
    "        if len(self.memory)>=self.capacity:\n",
    "          self.memory=self.memory[1:]+[transition]\n",
    "        else:\n",
    "          self.memory+=[transition]\n",
    "\n",
    "    def sample(self, batch_size):\n",
    "        return list(random.choices(self.memory,k=batch_size))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.memory)\n",
    "#################################################################################\n",
    "#                                   THE END                                     #\n",
    "#################################################################################  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BAsgNXi5PTwc"
   },
   "source": [
    "### 2.2. Network Declaration (4 Points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "class DQN(nn.Module):\n",
    "    def __init__(self, input_shape, num_actions):\n",
    "        super(DQN, self).__init__()\n",
    "        \n",
    "        self.input_shape = input_shape\n",
    "        self.num_actions = num_actions\n",
    "        #################################################################################\n",
    "        #                          COMPLETE THE FOLLOWING SECTION                       #\n",
    "        #################################################################################\n",
    "        # Initialize CNN Model :\n",
    "        # conv1: out_channels:32, kernel_size=8, stride=4\n",
    "        # conv2: out_channels:64, kernel_size=4, stride=2\n",
    "        # conv3: out_channels:64, kernel_size=3, stride=1\n",
    "        # fc1(512)\n",
    "        # fc2(512)\n",
    "        #################################################################################\n",
    "        self.conv1=nn.Conv2d(1,32,8,stride=4)#=> 32x20x20\n",
    "        self.conv2=nn.Conv2d(32,64,4,stride=2)#=>64x9x9\n",
    "        self.conv3=nn.Conv2d(64,64,3,stride=1)#=>64x7x7\n",
    "        self.flat=nn.Flatten()\n",
    "        self.fc1=nn.Linear(64*7*7,512)\n",
    "        self.fc2=nn.Linear(512,num_actions)\n",
    "        #################################################################################\n",
    "        #                                   THE END                                     #\n",
    "        #################################################################################\n",
    "\n",
    "    def forward(self, x):\n",
    "        #################################################################################\n",
    "        #                          COMPLETE THE FOLLOWING SECTION                       #\n",
    "        #################################################################################\n",
    "        # complete forward pass using initialized CNN Model. use Relu activation function \n",
    "        # for conv1, conv2, conv3, and fc1.  \n",
    "        #################################################################################\n",
    "        x=F.relu(self.conv1(x))\n",
    "        x=F.relu(self.conv2(x))\n",
    "        x=F.relu(self.conv3(x))\n",
    "        x=self.flat(x)\n",
    "        x=F.relu(self.fc1(x))\n",
    "        x=self.fc2(x)\n",
    "        return x\n",
    "        #################################################################################\n",
    "        #                                   THE END                                     #\n",
    "        #################################################################################   \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gbvmYNe_NkcY"
   },
   "source": [
    "### 2.3. Wrapper\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BfL-2Xe-YVJX"
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   "source": [
    "class WrapPyTorch(gym.ObservationWrapper):\n",
    "    def __init__(self, env=None):\n",
    "        super(WrapPyTorch, self).__init__(env)\n",
    "        obs_shape = self.observation_space.shape\n",
    "        self.observation_space = Box(\n",
    "            self.observation_space.low[0, 0, 0],\n",
    "            self.observation_space.high[0, 0, 0],\n",
    "            [obs_shape[2], obs_shape[1], obs_shape[0]],\n",
    "            dtype=self.observation_space.dtype)\n",
    "\n",
    "    def observation(self, observation):\n",
    "        return observation.transpose(2, 0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KmaplGeXPTwf"
   },
   "source": [
    " ### 2.4. Agent (15 Points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "class Model():\n",
    "    def __init__(self, env=None, log_dir='tmp/gym'):\n",
    "        self.device = device\n",
    "\n",
    "        self.gamma = GAMMA \n",
    "        self.lr = LR\n",
    "        self.target_net_update_freq = TARGET_NET_UPDATE_FREQ\n",
    "        self.experience_replay_size = EXP_REPLAY_SIZE\n",
    "        self.batch_size = BATCH_SIZE\n",
    "        self.learn_start = LEARN_START\n",
    "        self.update_freq = UPDATE_FREQ\n",
    "        self.log_dir = log_dir\n",
    "        self.rewards = []\n",
    "        self.action_log_frequency = ACTION_SELECTION_COUNT_FREQUENCY\n",
    "        #################################################################################\n",
    "        #                          COMPLETE THE FOLLOWING SECTION                       #\n",
    "        #################################################################################\n",
    "        # do followings line by line:\n",
    "        # * make a list of action selections\n",
    "        # * use shape of observation space to save number of features\n",
    "        # * save naumber of actions\n",
    "        # * save environment in self.env\n",
    "        # * use DQN class to declare model and target model (the game is 2-player game, so\n",
    "        #   we declare 2 model)\n",
    "        # * using load_state_dict, share learnable parameters (i.e. weights and biases) of\n",
    "        #   self.model with self.target_model \n",
    "        # * use Adam optimizer\n",
    "        #################################################################################\n",
    "        self.action_selections = [0.]*6\n",
    "        self.num_feats = env.observation_space\n",
    "        self.num_actions = 6\n",
    "        self.env = env\n",
    "\n",
    "        self.model = DQN(self.num_feats,self.num_actions)\n",
    "        self.target_model = DQN(self.num_feats,self.num_actions)\n",
    "            \n",
    "        self.target_model.load_state_dict(self.model.state_dict())\n",
    "\n",
    "        self.optimizer = optim.Adam(self.model.parameters(),self.lr)\n",
    "        #################################################################################\n",
    "        #                                   THE END                                     #\n",
    "        #################################################################################\n",
    "\n",
    "        # Move to correct device\n",
    "        self.model = self.model.to(self.device)\n",
    "        self.target_model.to(self.device)\n",
    "\n",
    "        self.model.train()\n",
    "        self.target_model.train()\n",
    "\n",
    "        self.update_count = 0\n",
    "\n",
    "        self.memory = ExperienceReplayMemory(self.experience_replay_size)\n",
    "        \n",
    "\n",
    "    def prep_minibatch(self):\n",
    "      \n",
    "        #################################################################################\n",
    "        #                          COMPLETE THE FOLLOWING SECTION                       #\n",
    "        #################################################################################\n",
    "        # random transition batch is taken from experience replay memory\n",
    "        # do followings line by line:\n",
    "        # * sample from self.memory with batch size, and save result in transitions, \n",
    "        #   indices, weights\n",
    "        # * use transitions to save batch_state, batch_action, batch_reward, \n",
    "        #   batch_next_state as tensors\n",
    "        # * save non_final_mask,  non_final_next_states as tensors, note that sometimes \n",
    "        #   all next states are false\n",
    "        #################################################################################\n",
    "        indices, weights = None,None\n",
    "        transitions=self.memory.sample(self.batch_size)\n",
    "\n",
    "        batch_state = torch.cat([t[0] for t in transitions])\n",
    "        batch_action = torch.tensor([t[1] for t in transitions],device=device)\n",
    "        batch_reward = torch.tensor([t[2] for t in transitions],device=device,dtype=torch.float32)\n",
    "        batch_next_state= torch.cat([torch.tensor(t[3],dtype=torch.float32,device=device).unsqueeze(dim=0) if t[3] is not None else torch.zeros(t[0].shape,dtype=torch.float32,device=device) for t in transitions])\n",
    "        non_final_mask = torch.tensor([0. if t[3] is None else 1. for t in transitions])\n",
    "\n",
    "        try: \n",
    "            non_final_next_states = torch.cat([t[3].unsqueeze(dim=0) for t in transitions if t[3] is not None])\n",
    "            empty_next_state_values = False\n",
    "        except:\n",
    "            non_final_next_states = None\n",
    "            empty_next_state_values = True\n",
    "        #################################################################################\n",
    "        #                                   THE END                                     #\n",
    "        #################################################################################\n",
    "        return batch_state, batch_action, batch_reward, non_final_next_states, non_final_mask, empty_next_state_values, indices, weights\n",
    "\n",
    "    def compute_loss(self, batch_vars):\n",
    "        batch_state, batch_action, batch_reward, non_final_next_states, non_final_mask, empty_next_state_values, indices, weights = batch_vars\n",
    "\n",
    "        #################################################################################\n",
    "        #                          COMPLETE THE FOLLOWING SECTION                       #\n",
    "        #################################################################################\n",
    "        # implement calculation of loss (you can use \"with torch.no_grad():\")\n",
    "        #################################################################################\n",
    "        Qs=self.model(batch_state)\n",
    "        if empty_next_state_values:\n",
    "          loss=F.mse_loss(Qs[torch.arange(0,batch_state.size(0),device=device),batch_action],batch_reward)\n",
    "        else:\n",
    "          Qsp=self.get_max_next_state_action(non_final_next_states).detach()\n",
    "          non_final_state_index=0\n",
    "          loss=0.\n",
    "          for i in range(batch_state.size(0)):\n",
    "            if non_final_mask[i]:\n",
    "              loss+=F.mse_loss(Qs[i],batch_reward[i]+self.gamma*Qsp[non_final_state_index])\n",
    "              non_final_state_index+=1\n",
    "            else:\n",
    "              loss+=F.mse_loss(Qs[i],batch_reward[i])\n",
    "          loss/=float(batch_state.size(0))\n",
    "        #################################################################################\n",
    "        #                                   THE END                                     #\n",
    "        #################################################################################\n",
    "        return loss\n",
    "\n",
    "    def update(self, s, a, r, s_, frame=0):\n",
    "        #################################################################################\n",
    "        #                          COMPLETE THE FOLLOWING SECTION                       #\n",
    "        #################################################################################\n",
    "        # implement update method to optimize model \n",
    "        # * push state, action, reward and new state to memoty\n",
    "        # * note that if frame is lower than self.learn_start or frame % self.update_freq != 0\n",
    "        #   return None\n",
    "        # * take a random transition batch and compute loss\n",
    "        # * optimize the model\n",
    "        #################################################################################\n",
    "        self.memory.push((s,a,r,s_))\n",
    "        if frame<self.learn_start or frame%self.update_freq!=0:\n",
    "          return None\n",
    "        self.model.zero_grad()\n",
    "        loss=self.compute_loss(self.prep_minibatch())\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "        #################################################################################\n",
    "        #                                   THE END                                     #\n",
    "        #################################################################################\n",
    "\n",
    "        self.update_target_model()       \n",
    "        self.save_td(loss.item(), frame)\n",
    "        self.save_sigma_param_magnitudes(frame)\n",
    "\n",
    "    def get_action(self, s, eps=0.1):\n",
    "\n",
    "        #################################################################################\n",
    "        #                          COMPLETE THE FOLLOWING SECTION                       #\n",
    "        #################################################################################  \n",
    "        # implement get_action method (epsilon greedy)\n",
    "        # you can use \"with torch.no_grad():\"\n",
    "        ################################################################################# \n",
    "        with torch.no_grad():\n",
    "          Qs=self.model(s)\n",
    "          if np.random.rand() < eps:\n",
    "            return torch.randint(self.num_actions,(s.size(0),))\n",
    "          else:\n",
    "            return torch.argmax(Qs,dim=1)\n",
    "        #################################################################################\n",
    "        #                                   THE END                                     #\n",
    "        #################################################################################\n",
    "\n",
    "    def update_target_model(self):\n",
    "      # update target model:\n",
    "        self.update_count+=1\n",
    "        self.update_count = self.update_count % self.target_net_update_freq\n",
    "        if self.update_count == 0:\n",
    "            self.target_model.load_state_dict(self.model.state_dict())\n",
    "\n",
    "    def get_max_next_state_action(self, next_states):\n",
    "        return self.target_model(next_states).max(dim=1)[1].view(-1, 1)\n",
    "\n",
    "\n",
    "    def save_sigma_param_magnitudes(self, tstep):\n",
    "        with torch.no_grad():\n",
    "            sum_, count = 0.0, 0.0\n",
    "            for name, param in self.model.named_parameters():\n",
    "                if param.requires_grad and 'sigma' in name:\n",
    "                    sum_+= torch.sum(param.abs()).item()\n",
    "                    count += np.prod(param.shape)\n",
    "            \n",
    "            if count > 0:\n",
    "                with open(os.path.join(self.log_dir, 'sig_param_mag.csv'), 'a') as f:\n",
    "                    writer = csv.writer(f)\n",
    "                    writer.writerow((tstep, sum_/count))\n",
    "\n",
    "    def save_td(self, td, tstep):\n",
    "      # save temporal difference\n",
    "        with open(os.path.join(self.log_dir, 'td.csv'), 'a') as f:\n",
    "            writer = csv.writer(f)\n",
    "            writer.writerow((tstep, td))\n",
    "\n",
    "    def save_reward(self, reward):\n",
    "        self.rewards.append(reward)\n",
    "\n",
    "    def save_action(self, action, tstep):\n",
    "        self.action_selections[int(action)] += 1.0/self.action_log_frequency\n",
    "        if (tstep+1) % self.action_log_frequency == 0:\n",
    "            with open(os.path.join(self.log_dir, 'action_log.csv'), 'a') as f:\n",
    "                writer = csv.writer(f)\n",
    "                writer.writerow(list([tstep]+self.action_selections))\n",
    "            self.action_selections = [0 for _ in range(len(self.action_selections))]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "COg8LTslPTwi"
   },
   "source": [
    " ## 3. Training Loop (8 Points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 346127,
     "status": "error",
     "timestamp": 1592061225480,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "yOmkex6lPTwj",
    "outputId": "7c3fb94d-708a-4869-c8f3-ae8bbefb70df"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-6d18279342a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0mepisode_reward\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0mreward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprev_observation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mobservation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mframe_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-21-a370692b51aa>\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, s, a, r, s_, frame)\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprep_minibatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m         \u001b[0;31m#################################################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;31m#                                   THE END                                     #\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m     98\u001b[0m                 \u001b[0;31m# Decay the first and second moment running average coefficient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m                 \u001b[0mexp_avg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m                 \u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddcmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m                     \u001b[0;31m# Maintains the maximum of all 2nd moment running avg. till now\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "start=timer()\n",
    "\n",
    "log_dir = \"tmp/gym/\"\n",
    "try:\n",
    "    os.makedirs(log_dir)\n",
    "except OSError:\n",
    "    files = glob.glob(os.path.join(log_dir, '*.monitor.csv')) \\\n",
    "        + glob.glob(os.path.join(log_dir, '*td.csv')) \\\n",
    "        + glob.glob(os.path.join(log_dir, '*sig_param_mag.csv')) \\\n",
    "        + glob.glob(os.path.join(log_dir, '*action_log.csv'))\n",
    "    for f in files:\n",
    "        os.remove(f)\n",
    "\n",
    "#################################################################################\n",
    "#                          COMPLETE THE FOLLOWING SECTION                       #\n",
    "#################################################################################\n",
    "# do followings line by line:\n",
    "# * use \"make_atari\" wrapper and save \"PongNoFrameskip-v4\" game to env\n",
    "# * use \"bench.Monitor\" wrapper to know the episode reward, length, time and other data.\n",
    "# * use \"wrap_deepmind\" wrapper to configure environment for DeepMind-style Atari.\n",
    "# * use *WrapPyTorch*\n",
    "# * save model\n",
    "# * implement training loop\n",
    "#################################################################################\n",
    "env_id = \"PongNoFrameskip-v4\"\n",
    "env =  WrapPyTorch(wrap_deepmind(bench.Monitor(make_atari(env_id),os.path.join(log_dir,'monitor'))))\n",
    "\n",
    "model  = Model(env)\n",
    "\n",
    "episode_reward = 0\n",
    "observation = env.reset()\n",
    "for frame_idx in range(1, MAX_FRAMES + 1):\n",
    "    epsilon = epsilon_by_frame(frame_idx)\n",
    "    observation=torch.tensor(observation,dtype=torch.float32,device=device).unsqueeze(dim=0)\n",
    "    action = model.get_action(observation,eps=epsilon)\n",
    "    \n",
    "    model.save_action(action, frame_idx) #log action selection\n",
    "\n",
    "    prev_observation = observation\n",
    "    observation, reward, done, _ = env.step(action)\n",
    "    episode_reward+=reward\n",
    "\n",
    "    model.update(prev_observation,action,reward,None if done else observation,frame=frame_idx)\n",
    "\n",
    "    if done:\n",
    "        observation=env.reset()\n",
    "        model.save_reward(episode_reward)\n",
    "        episode_reward=0\n",
    "        \n",
    "#################################################################################\n",
    "#                                   THE END                                     #\n",
    "################################################################################# \n",
    "    if frame_idx % 1000 == 0:\n",
    "        try:\n",
    "            clear_output(True)\n",
    "            plot_all_data(log_dir, env_id, 'DQN', MAX_FRAMES, bin_size=(10, 100, 100, 1), smooth=1, time=timedelta(seconds=int(timer()-start)), ipynb=True)\n",
    "        except IOError:\n",
    "            pass\n",
    " \n",
    "env.close()\n",
    "plot_all_data(log_dir, env_id, 'DQN', MAX_FRAMES, bin_size=(10, 100, 100, 1), smooth=1, time=timedelta(seconds=int(timer()-start)), ipynb=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bqc_dDMIVxks"
   },
   "source": [
    "![Result](result.JPG)"
   ]
  }
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