diff --git a/src/primaite/game/policy/__init__.py b/src/primaite/game/policy/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/primaite/game/policy/policy.py b/src/primaite/game/policy/policy.py
new file mode 100644
index 00000000..8d5a9a08
--- /dev/null
+++ b/src/primaite/game/policy/policy.py
@@ -0,0 +1,58 @@
+from abc import ABC, abstractclassmethod, abstractmethod
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from primaite.game.session import PrimaiteSession
+
+
+class PolicyABC(ABC):
+    """Base class for reinforcement learning agents."""
+
+    @abstractmethod
+    def __init__(self, session: "PrimaiteSession") -> None:
+        """Initialize a reinforcement learning agent."""
+        self.session: "PrimaiteSession" = session
+        pass
+
+    @abstractmethod
+    def learn(
+        self,
+    ) -> None:
+        """Train the agent."""
+        pass
+
+    @abstractmethod
+    def eval(
+        self,
+    ) -> None:
+        """Evaluate the agent."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+    ) -> None:
+        """Save the agent."""
+        pass
+
+    @abstractmethod
+    def load(
+        self,
+    ) -> None:
+        """Load agent from a file."""
+        pass
+
+    def close(
+        self,
+    ) -> None:
+        """Close the agent."""
+        pass
+
+    @abstractclassmethod
+    def from_config(
+        cls,
+    ) -> "PolicyABC":
+        """Create an agent from a config file."""
+        pass
+
+    # saving checkpoints logic will be handled here, it will invoke 'save' method which is implemented by the subclass
diff --git a/src/primaite/game/policy/sb3.py b/src/primaite/game/policy/sb3.py
new file mode 100644
index 00000000..9c6b49ae
--- /dev/null
+++ b/src/primaite/game/policy/sb3.py
@@ -0,0 +1,89 @@
+from typing import Literal, TYPE_CHECKING, Union
+
+from stable_baselines3 import A2C, PPO
+from stable_baselines3.a2c import MlpPolicy as A2C_MLP
+from stable_baselines3.ppo import MlpPolicy as PPO_MLP
+
+from primaite.game.policy.policy import PolicyABC
+
+if TYPE_CHECKING:
+    from primaite.game.session import PrimaiteSession
+
+
+class SB3Policy(PolicyABC):
+    """Single agent RL policy using stable baselines 3."""
+
+    def __init__(self, session: "PrimaiteSession", algorithm: Literal["PPO", "A2C"]):
+        """Initialize a stable baselines 3 policy."""
+        super().__init__(session=session)
+
+        self._agent_class: type[Union[PPO, A2C]]
+        if algorithm == "PPO":
+            self._agent_class = PPO
+            policy = PPO_MLP
+        elif algorithm == "A2C":
+            self._agent_class = A2C
+            policy = A2C_MLP
+        else:
+            raise ValueError(f"Unknown algorithm `{algorithm}` for stable_baselines3 policy")
+        self._agent = self._agent_class(
+            policy=policy,
+            env=self.session.env,
+            n_steps=...,
+            seed=...,
+        )  # TODO: populate values once I figure out how to get them from the config / session
+
+    def learn(
+        self,
+    ) -> None:
+        """Train the agent."""
+        time_steps = 9999  # TODO: populate values once I figure out how to get them from the config / session
+        episodes = 10  # TODO: populate values once I figure out how to get them from the config / session
+        for i in range(episodes):
+            self._agent.learn(total_timesteps=time_steps)
+            self._save_checkpoint()
+        pass
+
+    def eval(
+        self,
+    ) -> None:
+        """Evaluate the agent."""
+        time_steps = 9999  # TODO: populate values once I figure out how to get them from the config / session
+        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
+
+        for episode in range(num_episodes):
+            obs = self.session.env.reset()
+            for step in range(time_steps):
+                action, _states = self._agent.predict(obs, deterministic=deterministic)
+                obs, rewards, truncated, terminated, info = self.session.env.step(action)
+
+    def save(
+        self,
+    ) -> None:
+        """Save the agent."""
+        savepath = (
+            "temp/path/to/save.pth"  # TODO: populate values once I figure out how to get them from the config / session
+        )
+        self._agent.save(savepath)
+        pass
+
+    def load(
+        self,
+    ) -> None:
+        """Load agent from a checkpoint."""
+        self._agent_class.load("temp/path/to/save.pth", env=self.session.env)
+        pass
+
+    def close(
+        self,
+    ) -> None:
+        """Close the agent."""
+        pass
+
+    @classmethod
+    def from_config(
+        self,
+    ) -> "SB3Policy":
+        """Create an agent from config file."""
+        pass