Make environment reset reinstantiate the game

src/primaite/game/game.py

@@ -67,9 +67,6 @@ class PrimaiteGame:
         self.step_counter: int = 0
         """Current timestep within the episode."""
 
-        self.episode_counter: int = 0
-        """Current episode number."""
-
         self.options: PrimaiteGameOptions
         """Special options that apply for the entire game."""
 
@@ -163,7 +160,7 @@ class PrimaiteGame:
             return True
         return False
 
-    def reset(self) -> None:
+    def reset(self) -> None:  # TODO: deprecated - remove me
         """Reset the game, this will reset the simulation."""
         self.episode_counter += 1
         self.step_counter = 0

src/primaite/notebooks/training_example_sb3.ipynb

@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "gym = PrimaiteGymEnv(game=game)"
+    "gym = PrimaiteGymEnv(game_config=cfg)"
    ]
   },
   {
@@ -65,7 +65,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.learn(total_timesteps=1000)\n"
+    "model.learn(total_timesteps=10)\n"
    ]
   },
   {

src/primaite/session/environment.py

@@ -18,11 +18,18 @@ class PrimaiteGymEnv(gymnasium.Env):
     assumptions about the agent list always having a list of length 1.
     """
 
-    def __init__(self, game: PrimaiteGame):
+    def __init__(self, game_config: Dict):
         """Initialise the environment."""
         super().__init__()
-        self.game: "PrimaiteGame" = game
+        self.game_config: Dict = game_config
+        """PrimaiteGame definition. This can be changed between episodes to enable curriculum learning."""
+        self.game: PrimaiteGame = PrimaiteGame.from_config(self.game_config)
+        """Current game."""
         self.agent: ProxyAgent = self.game.rl_agents[0]
+        """The agent within the game that is controlled by the RL algorithm."""
+
+        self.episode_counter: int = 0
+        """Current episode number."""
 
     def step(self, action: ActType) -> Tuple[ObsType, SupportsFloat, bool, bool, Dict[str, Any]]:
         """Perform a step in the environment."""
@@ -45,13 +52,13 @@ class PrimaiteGymEnv(gymnasium.Env):
         return next_obs, reward, terminated, truncated, info
 
     def _write_step_metadata_json(self, action: int, state: Dict, reward: int):
-        output_dir = SIM_OUTPUT.path / f"episode_{self.game.episode_counter}" / "step_metadata"
+        output_dir = SIM_OUTPUT.path / f"episode_{self.episode_counter}" / "step_metadata"
 
         output_dir.mkdir(parents=True, exist_ok=True)
         path = output_dir / f"step_{self.game.step_counter}.json"
 
         data = {
-            "episode": self.game.episode_counter,
+            "episode": self.episode_counter,
             "step": self.game.step_counter,
             "action": int(action),
             "reward": int(reward),
@@ -63,10 +70,12 @@ class PrimaiteGymEnv(gymnasium.Env):
     def reset(self, seed: Optional[int] = None) -> Tuple[ObsType, Dict[str, Any]]:
         """Reset the environment."""
         print(
-            f"Resetting environment, episode {self.game.episode_counter}, "
+            f"Resetting environment, episode {self.episode_counter}, "
             f"avg. reward: {self.game.rl_agents[0].reward_function.total_reward}"
         )
-        self.game.reset()
+        self.game: PrimaiteGame = PrimaiteGame.from_config(cfg=self.game_config)
+        self.agent = self.game.rl_agents[0]
+        self.episode_counter += 1
         state = self.game.get_sim_state()
         self.game.update_agents(state)
         next_obs = self._get_obs()
@@ -107,7 +116,7 @@ class PrimaiteRayEnv(gymnasium.Env):
         :type env_config: Dict[str, PrimaiteGame]
         """
         self.env = PrimaiteGymEnv(game=PrimaiteGame.from_config(env_config["cfg"]))
-        self.env.game.episode_counter -= 1
+        self.env.episode_counter -= 1
         self.action_space = self.env.action_space
         self.observation_space = self.env.observation_space
 
@@ -194,13 +203,13 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
         return next_obs, rewards, terminateds, truncateds, infos
 
     def _write_step_metadata_json(self, actions: Dict, state: Dict, rewards: Dict):
-        output_dir = SIM_OUTPUT.path / f"episode_{self.game.episode_counter}" / "step_metadata"
+        output_dir = SIM_OUTPUT.path / f"episode_{self.episode_counter}" / "step_metadata"
 
         output_dir.mkdir(parents=True, exist_ok=True)
         path = output_dir / f"step_{self.game.step_counter}.json"
 
         data = {
-            "episode": self.game.episode_counter,
+            "episode": self.episode_counter,
             "step": self.game.step_counter,
             "actions": {agent_name: int(action) for agent_name, action in actions.items()},
             "reward": rewards,