diff --git a/src/primaite/agents/agent.py b/src/primaite/agents/agent.py
index 685fe776..32118597 100644
--- a/src/primaite/agents/agent.py
+++ b/src/primaite/agents/agent.py
@@ -257,10 +257,19 @@ 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):
diff --git a/src/primaite/agents/rllib.py b/src/primaite/agents/rllib.py
index d851ba9c..427072c4 100644
--- a/src/primaite/agents/rllib.py
+++ b/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
@@ -120,9 +122,11 @@ 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,
@@ -140,6 +144,7 @@ class RLlibAgent(AgentSessionABC):
         for i in range(episodes):
             self._current_result = self._agent.train()
             self._save_checkpoint()
+        self.save()
         self._agent.stop()
         super().learn()
 
@@ -162,9 +167,25 @@ 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."""
diff --git a/src/primaite/agents/sb3.py b/src/primaite/agents/sb3.py
index f5ac44cb..18e208e4 100644
--- a/src/primaite/agents/sb3.py
+++ b/src/primaite/agents/sb3.py
@@ -64,11 +64,13 @@ class SB3Agent(AgentSessionABC):
     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
@@ -90,6 +92,7 @@ class SB3Agent(AgentSessionABC):
             self._agent.learn(total_timesteps=time_steps)
             self._save_checkpoint()
         self._env.reset()
+        self.save()
         self._env.close()
         super().learn()
 
@@ -134,7 +137,7 @@ 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."""
diff --git a/tests/test_primaite_session.py b/tests/test_primaite_session.py
index ae0b0870..75ea5882 100644
--- a/tests/test_primaite_session.py
+++ b/tests/test_primaite_session.py
@@ -33,6 +33,9 @@ def test_primaite_session(temp_primaite_session):
         # Check that the network png file exists
         assert (session_path / f"network_{session.timestamp_str}.png").exists()
 
+        # Check that the saved agent exists
+        assert session._agent_session._saved_agent_path.exists()
+
         # Check that both the transactions and av reward csv files exist
         for file in session.learning_path.iterdir():
             if file.suffix == ".csv":