adding mp with trained model example

examples/mp_with_trained_agent_env.py

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+#!/usr/bin/env python3
+import diambra.arena
+from diambra.arena import SpaceTypes, EnvironmentSettingsMultiAgent
+from stable_baselines3 import PPO
+import numpy as np
+
+def main():
+    # Environment Settings
+    settings = EnvironmentSettingsMultiAgent() # Multi Agents environment
+
+    # --- Environment settings ---
+
+    # If to use discrete or multi_discrete action space
+    settings.action_space = (SpaceTypes.DISCRETE, SpaceTypes.DISCRETE)
+
+    # --- Episode settings ---
+
+    # Characters to be used, automatically extended with None for games
+    # requiring to select more than one character (e.g. Tekken Tag Tournament)
+    settings.characters = ("Ken", "Ken")
+
+    # Characters outfit
+    settings.outfits = (2, 2)
+
+    # Create environment
+    env = diambra.arena.make("sfiii3n", settings, render_mode="human")
+
+    # Load model
+    model_path = "/path/to/model"
+
+    # Load agent without passing the environment
+    agent = PPO.load(model_path)
+
+    # Begin evaluation
+    observation, info = env.reset(seed=42)
+    env.show_obs(observation)
+
+    while True:
+        env.render()
+
+        # Extract observations for player 1 (P1), including shared environment information
+        observation_p1 = {
+            key: value for key, value in observation.items()
+            if key.startswith('P1_') or key in ['frame', 'stage']
+        }
+
+        # Initialize player 2 (P2) observation with shared environment information
+        observation_p2 = {'frame': observation['frame'], 'stage': observation['stage']}
+
+        # Swap P1 and P2 keys for P2 observation
+        # Modify P2 keys to match P1 format for the model, as it was trained with P1 observations
+        observation_p2.update({
+            key.replace('P2_', 'P1_'): value for key, value in observation.items()
+            if key.startswith('P2_')
+        })
+
+        # Model prediction for P1 actions based on P1 observation
+        action_p1, _ = agent.predict(observation_p1, deterministic=True)
+        # Model prediction for P2 actions, using modified P2 observation
+        action_p2, _ = agent.predict(observation_p2, deterministic=True)
+
+        # Combine actions for both players
+        actions = np.append(action_p1, action_p2)
+        print("Actions: {}".format(actions))
+
+        observation, reward, terminated, truncated, info = env.step(actions)
+        done = terminated or truncated
+        env.show_obs(observation)
+        print("Reward: {}".format(reward))
+        print("Done: {}".format(done))
+        print("Info: {}".format(info))
+
+        if done:
+            # Optionally, change episode settings here
+            options = {}
+            options["characters"] = (None, None)
+            options["char_outfits"] = (5, 5)
+            observation, info = env.reset(options=options)
+            env.show_obs(observation)
+            break
+
+    env.close()
+
+    # Return success
+    return 0
+
+if __name__ == '__main__':
+    main()