#917 - Got RLlib fully training in PrimAITE. Started integrating the the other agents into the Session class

src/primaite/agents/sb3.py

@@ -6,30 +6,74 @@ from primaite.agents.agent_abc import AgentABC
 from primaite.environment.primaite_env import Primaite
 from stable_baselines3.ppo import MlpPolicy as PPOMlp
 
+
 class SB3PPO(AgentABC):
-    def __init__(self, env: Primaite):
-        super().__init__(env)
+    def __init__(
+            self,
+            training_config_path,
+            lay_down_config_path
+    ):
+        super().__init__(training_config_path, lay_down_config_path)
+        self._tensorboard_log_path = self.session_path / "tensorboard_logs"
+        self._tensorboard_log_path.mkdir(parents=True, exist_ok=True)
 
     def _setup(self):
+        self._env = Primaite(
+            training_config_path=self._training_config_path,
+            lay_down_config_path=self._lay_down_config_path,
+            transaction_list=[],
+            session_path=self.session_path,
+            timestamp_str=self.timestamp_str
+        )
         self._agent = PPO(
             PPOMlp,
             self._env,
             verbose=0,
-            n_steps=self._training_config.num_steps
+            n_steps=self._training_config.num_steps,
+            tensorboard_log=self._tensorboard_log_path
         )
 
+    def learn(
+            self,
+            time_steps: Optional[int] = None,
+            episodes: Optional[int] = None
+    ):
+        if not time_steps:
+            time_steps = self._training_config.num_steps
 
-    def learn(self, time_steps: Optional[int], episodes: Optional[int]):
-        pass
+        if not episodes:
+            episodes = self._training_config.num_episodes
 
-    def evaluate(self, time_steps: Optional[int], episodes: Optional[int]):
-        pass
+        for i in range(episodes):
+            self._agent.learn(total_timesteps=time_steps)
+
+    def evaluate(
+            self,
+            time_steps: Optional[int] = None,
+            episodes: Optional[int] = None,
+            deterministic: bool = True
+    ):
+        if not time_steps:
+            time_steps = self._training_config.num_steps
+
+        if not episodes:
+            episodes = self._training_config.num_episodes
+
+        for episode in range(episodes):
+            obs = self._env.reset()
+
+            for step in range(time_steps):
+                action, _states = self._agent.predict(
+                    obs,
+                    deterministic=deterministic
+                )
+                obs, rewards, done, info = self._env.step(action)
 
     def load(self):
-        pass
+        raise NotImplementedError
 
     def save(self):
-        pass
+        raise NotImplementedError
 
     def export(self):
-        pass
+        raise NotImplementedError