Merge remote-tracking branch 'origin/dev' into feature/1572-fix-docs-formatting

src/primaite/agents/agent.py

@@ -266,10 +266,19 @@ class AgentSessionABC(ABC):
             raise FileNotFoundError(msg)
         pass
 
+    @property
+    def _saved_agent_path(self) -> Path:
+        file_name = (
+            f"{self._training_config.agent_framework}_"
+            f"{self._training_config.agent_identifier}_"
+            f"{self.timestamp_str}.zip"
+        )
+        return self.learning_path / file_name
+
     @abstractmethod
     def save(self):
         """Save the agent."""
-        self._agent.save(self.session_path)
+        pass
 
     @abstractmethod
     def export(self):

src/primaite/agents/rllib.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
 
 import json
+import shutil
 from datetime import datetime
 from pathlib import Path
 from typing import Union
+from uuid import uuid4
 
 from ray.rllib.algorithms import Algorithm
 from ray.rllib.algorithms.a2c import A2CConfig
@@ -118,6 +120,7 @@ class RLlibAgent(AgentSessionABC):
                 timestamp_str=self.timestamp_str,
             ),
         )
+        self._agent_config.seed = self._training_config.seed
 
         self._agent_config.training(train_batch_size=self._training_config.num_steps)
         self._agent_config.framework(framework="tf")
@@ -132,9 +135,11 @@ class RLlibAgent(AgentSessionABC):
     def _save_checkpoint(self):
         checkpoint_n = self._training_config.checkpoint_every_n_episodes
         episode_count = self._current_result["episodes_total"]
-        if checkpoint_n > 0 and episode_count > 0:
-            if (episode_count % checkpoint_n == 0) or (episode_count == self._training_config.num_episodes):
-                self._agent.save(str(self.checkpoints_path))
+        save_checkpoint = False
+        if checkpoint_n:
+            save_checkpoint = episode_count % checkpoint_n == 0
+        if episode_count and save_checkpoint:
+            self._agent.save(str(self.checkpoints_path))
 
     def learn(
         self,
@@ -152,9 +157,14 @@ class RLlibAgent(AgentSessionABC):
         for i in range(episodes):
             self._current_result = self._agent.train()
             self._save_checkpoint()
+        self.save()
         self._agent.stop()
+
         super().learn()
 
+        # save agent
+        self.save()
+
     def evaluate(
         self,
         **kwargs,
@@ -174,9 +184,25 @@ class RLlibAgent(AgentSessionABC):
         """Load an agent from file."""
         raise NotImplementedError
 
-    def save(self):
+    def save(self, overwrite_existing: bool = True):
         """Save the agent."""
-        raise NotImplementedError
+        # Make temp dir to save in isolation
+        temp_dir = self.learning_path / str(uuid4())
+        temp_dir.mkdir()
+
+        # Save the agent to the temp dir
+        self._agent.save(str(temp_dir))
+
+        # Capture the saved Rllib checkpoint inside the temp directory
+        for file in temp_dir.iterdir():
+            checkpoint_dir = file
+            break
+
+        # Zip the folder
+        shutil.make_archive(str(self._saved_agent_path).replace(".zip", ""), "zip", checkpoint_dir)  # noqa
+
+        # Drop the temp directory
+        shutil.rmtree(temp_dir)
 
     def export(self):
         """Export the agent to transportable file format."""

src/primaite/agents/sb3.py

@@ -71,16 +71,19 @@ class SB3Agent(AgentSessionABC):
             verbose=self.sb3_output_verbose_level,
             n_steps=self._training_config.num_steps,
             tensorboard_log=str(self._tensorboard_log_path),
+            seed=self._training_config.seed,
         )
 
     def _save_checkpoint(self):
         checkpoint_n = self._training_config.checkpoint_every_n_episodes
         episode_count = self._env.episode_count
-        if checkpoint_n > 0 and episode_count > 0:
-            if (episode_count % checkpoint_n == 0) or (episode_count == self._training_config.num_episodes):
-                checkpoint_path = self.checkpoints_path / f"sb3ppo_{episode_count}.zip"
-                self._agent.save(checkpoint_path)
-                _LOGGER.debug(f"Saved agent checkpoint: {checkpoint_path}")
+        save_checkpoint = False
+        if checkpoint_n:
+            save_checkpoint = episode_count % checkpoint_n == 0
+        if episode_count and save_checkpoint:
+            checkpoint_path = self.checkpoints_path / f"sb3ppo_{episode_count}.zip"
+            self._agent.save(checkpoint_path)
+            _LOGGER.debug(f"Saved agent checkpoint: {checkpoint_path}")
 
     def _get_latest_checkpoint(self):
         pass
@@ -102,25 +105,27 @@ class SB3Agent(AgentSessionABC):
             self._agent.learn(total_timesteps=time_steps)
             self._save_checkpoint()
         self._env.reset()
+        self.save()
         self._env.close()
         super().learn()
 
+        # save agent
+        self.save()
+
     def evaluate(
         self,
-        deterministic: bool = True,
         **kwargs,
     ):
         """
         Evaluate the agent.
 
-        :param deterministic: Whether the evaluation is deterministic.
         :param kwargs: Any agent-specific key-word args to be passed.
         """
         time_steps = self._training_config.num_steps
         episodes = self._training_config.num_episodes
         self._env.set_as_eval()
         self.is_eval = True
-        if deterministic:
+        if self._training_config.deterministic:
             deterministic_str = "deterministic"
         else:
             deterministic_str = "non-deterministic"
@@ -131,7 +136,7 @@ class SB3Agent(AgentSessionABC):
             obs = self._env.reset()
 
             for step in range(time_steps):
-                action, _states = self._agent.predict(obs, deterministic=deterministic)
+                action, _states = self._agent.predict(obs, deterministic=self._training_config.deterministic)
                 if isinstance(action, np.ndarray):
                     action = np.int64(action)
                 obs, rewards, done, info = self._env.step(action)
@@ -146,7 +151,7 @@ class SB3Agent(AgentSessionABC):
 
     def save(self):
         """Save the agent."""
-        raise NotImplementedError
+        self._agent.save(self._saved_agent_path)
 
     def export(self):
         """Export the agent to transportable file format."""