Begin implementing training loop in session

src/primaite/game/policy/sb3.py

@@ -52,6 +52,7 @@ class SB3Policy(PolicyABC):
         num_episodes = 10  # TODO: populate values once I figure out how to get them from the config / session
         deterministic = True  # TODO: populate values once I figure out how to get them from the config / session
 
+        # TODO: consider moving this loop to the session, only if this makes sense for RAY RLLIB
         for episode in range(num_episodes):
             obs = self.session.env.reset()
             for step in range(time_steps):

src/primaite/game/session.py

@@ -9,6 +9,7 @@ from primaite.game.agent.actions import ActionManager
 from primaite.game.agent.interface import AbstractAgent, RandomAgent
 from primaite.game.agent.observations import ObservationSpace
 from primaite.game.agent.rewards import RewardFunction
+from primaite.game.policy.policy import PolicyABC
 from primaite.simulator.network.hardware.base import Link, NIC, Node
 from primaite.simulator.network.hardware.nodes.computer import Computer
 from primaite.simulator.network.hardware.nodes.router import ACLAction, Router
@@ -59,31 +60,51 @@ class PrimaiteSession:
     def __init__(self):
         self.simulation: Simulation = Simulation()
         """Simulation object with which the agents will interact."""
+
         self.agents: List[AbstractAgent] = []
         """List of agents."""
-        self.rl_agent: AbstractAgent
-        """The agent from the list which communicates with GATE to perform reinforcement learning."""
+
+        # self.rl_agent: AbstractAgent
+        # """The agent from the list which communicates with GATE to perform reinforcement learning."""
+
         self.step_counter: int = 0
         """Current timestep within the episode."""
+
         self.episode_counter: int = 0
         """Current episode number."""
+
         self.options: PrimaiteSessionOptions
         """Special options that apply for the entire game."""
+
         self.training_options: TrainingOptions
         """Options specific to agent training."""
 
+        self.policy: PolicyABC
+        """The reinforcement learning policy."""
+
         self.ref_map_nodes: Dict[str, Node] = {}
         """Mapping from unique node reference name to node object. Used when parsing config files."""
+
         self.ref_map_services: Dict[str, Service] = {}
         """Mapping from human-readable service reference to service object. Used for parsing config files."""
+
         self.ref_map_applications: Dict[str, Application] = {}
         """Mapping from human-readable application reference to application object. Used for parsing config files."""
+
         self.ref_map_links: Dict[str, Link] = {}
         """Mapping from human-readable link reference to link object. Used when parsing config files."""
 
     def start_session(self) -> None:
         """Commence the training session, this gives the GATE client control over the simulation/agent loop."""
-        raise NotImplementedError
+        # n_learn_steps = self.training_options.n_learn_steps
+        n_learn_episodes = self.training_options.n_learn_episodes
+        # n_eval_steps = self.training_options.n_eval_steps
+        n_eval_episodes = self.training_options.n_eval_episodes
+        if n_learn_episodes > 0:
+            self.policy.learn()
+
+        if n_eval_episodes > 0:
+            self.policy.eval()
 
     def step(self):
         """