Add ability to save sb3 final model

src/primaite/game/io.py

@@ -0,0 +1,54 @@
+from datetime import datetime
+from pathlib import Path
+from typing import Optional
+
+from pydantic import BaseModel
+
+from primaite import PRIMAITE_PATHS
+
+
+class SessionIOSettings(BaseModel):
+    """Schema for session IO settings."""
+
+    save_final_model: bool = True
+    """Whether to save the final model right at the end of training."""
+    save_checkpoints: bool = False
+    """Whether to save a checkpoint model every `checkpoint_interval` episodes"""
+    checkpoint_interval: int = 10
+    """How often to save a checkpoint model (if save_checkpoints is True)."""
+    save_logs: bool = True
+    """Whether to save logs"""
+    save_transactions: bool = True
+    """Whether to save transactions, If true, the session path will have a transactions folder."""
+    save_tensorboard_logs: bool = False
+    """Whether to save tensorboard logs. If true, the session path will have a tenorboard_logs folder."""
+
+
+class SessionIO:
+    """
+    Class for managing session IO.
+
+    Currently it's handling path generation, but could expand to handle loading, transaction, tensorboard, and so on.
+    """
+
+    def __init__(self, settings: SessionIOSettings = SessionIOSettings()) -> None:
+        self.settings = settings
+        self.session_path = self.generate_session_path()
+
+    def generate_session_path(self, timestamp: Optional[datetime] = None) -> Path:
+        """Create a folder for the session and return the path to it."""
+        if timestamp is None:
+            timestamp = datetime.now()
+        date_str = timestamp.strftime("%Y-%m-%d")
+        time_str = timestamp.strftime("%H-%M-%S")
+        session_path = PRIMAITE_PATHS.user_sessions_path / date_str / time_str
+        session_path.mkdir(exist_ok=True, parents=True)
+        return session_path
+
+    def generate_model_save_path(self, agent_name: str) -> Path:
+        """Return the path where the final model will be saved (excluding filename extension)."""
+        return self.session_path / "checkpoints" / f"{agent_name}_final"
+
+    def generate_checkpoint_save_path(self, agent_name: str, episode: int) -> Path:
+        """Return the path where the checkpoint model will be saved (excluding filename extension)."""
+        return self.session_path / "checkpoints" / f"{agent_name}_checkpoint_{episode}.pt"

src/primaite/game/policy/policy.py

@@ -1,5 +1,6 @@
 """Base class and common logic for RL policies."""
 from abc import ABC, abstractmethod
+from pathlib import Path
 from typing import Any, Dict, TYPE_CHECKING
 
 if TYPE_CHECKING:
@@ -54,7 +55,7 @@ class PolicyABC(ABC):
         pass
 
     @abstractmethod
-    def save(self) -> None:
+    def save(self, save_path: Path) -> None:
         """Save the agent."""
         pass
 

src/primaite/game/policy/sb3.py

@@ -1,4 +1,5 @@
 """Stable baselines 3 policy."""
+from pathlib import Path
 from typing import Literal, Optional, TYPE_CHECKING, Union
 
 from stable_baselines3 import A2C, PPO
@@ -50,12 +51,15 @@ class SB3Policy(PolicyABC, identifier="SB3"):
         )
         print(reward_data)
 
-    def save(self) -> None:
+    def save(self, save_path: Path) -> None:
-        """Save the agent."""
+        """
-        savepath = (
+        Save the current policy parameters.
-            "temp/path/to/save.pth"  # TODO: populate values once I figure out how to get them from the config / session
+
-        )
+        Warning: The recommended way to save model checkpoints is to use a callback within the `learn()` method. Please
-        self._agent.save(savepath)
+        refer to https://stable-baselines3.readthedocs.io/en/master/guide/callbacks.html for more information.
+        Therefore, this method is only used to save the final model.
+        """
+        self._agent.save(save_path)
         pass
 
     def load(self) -> None:

src/primaite/game/session.py

@@ -13,6 +13,7 @@ from primaite.game.agent.actions import ActionManager
 from primaite.game.agent.interface import AbstractAgent, ProxyAgent, RandomAgent
 from primaite.game.agent.observations import ObservationManager
 from primaite.game.agent.rewards import RewardFunction
+from primaite.game.io import SessionIO, SessionIOSettings
 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
@@ -179,6 +180,10 @@ class PrimaiteSession:
         """evaluation episodes counter"""
 
         self.mode: SessionMode = SessionMode.MANUAL
+        """Current session mode."""
+
+        self.io_manager = SessionIO()
+        """IO manager for the session."""
 
     def start_session(self) -> None:
         """Commence the training session."""
@@ -190,6 +195,7 @@ class PrimaiteSession:
         n_eval_episodes = self.training_options.n_eval_episodes
         deterministic_eval = self.training_options.deterministic_eval
         self.policy.learn(n_time_steps=n_learn_steps)
+        self.save_models()
 
         self.mode = SessionMode.EVAL
         if n_eval_episodes > 0:
@@ -198,6 +204,11 @@ class PrimaiteSession:
 
         self.mode = SessionMode.MANUAL
 
+    def save_models(self) -> None:
+        """Save the RL models."""
+        save_path = self.io_manager.generate_model_save_path("temp_model_name")
+        self.policy.save(save_path)
+
     def step(self):
         """
         Perform one step of the simulation/agent loop.
@@ -500,4 +511,8 @@ class PrimaiteSession:
         # CREATE POLICY
         sess.policy = PolicyABC.from_config(sess.training_options, session=sess)
 
+        # READ IO SETTINGS
+        io_settings = cfg.get("io_settings", {})
+        sess.io_manager.settings = SessionIO(settings=SessionIOSettings(**io_settings))
+
         return sess