Updated TODO

README.md

@@ -9,7 +9,7 @@
 Fancy RL provides a minimalistic and efficient implementation of Proximal Policy Optimization (PPO) and Trust Region Policy Layers (TRPL) using primitives from [torchrl](https://pypi.org/project/torchrl/). This library focuses on providing clean, understandable code and reusable modules while leveraging the powerful functionalities of torchrl.
 
 | :exclamation: This project is still WIP and not ready to be used. |
-| ------------------------------------------------------------ |
+| ----------------------------------------------------------------- |
 
 ## Installation
 
@@ -54,6 +54,7 @@ pytest test/test_ppo.py
 
 ## TODO
 
+- [ ] Better / more logging
 - [ ] Test / Benchmark PPO
 - [ ] Refactor Modules for TRPL
 - [ ] Get TRPL working

fancy_rl/algos/algo.py

@@ -0,0 +1,75 @@
+import torch
+import gymnasium as gym
+from torchrl.envs.libs.gym import GymWrapper
+from torchrl.record import VideoRecorder
+from abc import ABC
+
+from fancy_rl.loggers import TerminalLogger
+
+class Algo(ABC):
+    def __init__(
+        self,
+        env_spec,
+        loggers,
+        optimizers,
+        learning_rate,
+        n_steps,
+        batch_size,
+        n_epochs,
+        gamma,
+        total_timesteps,
+        eval_interval,
+        eval_deterministic,
+        entropy_coef,
+        critic_coef,
+        normalize_advantage,
+        device=None,
+        eval_episodes=10,
+        env_spec_eval=None,
+    ):
+        self.env_spec = env_spec
+        self.env_spec_eval = env_spec_eval if env_spec_eval is not None else env_spec
+        self.loggers = loggers if loggers != None else [TerminalLogger(None, None)]
+        self.optimizers = optimizers
+        self.learning_rate = learning_rate
+        self.n_steps = n_steps
+        self.batch_size = batch_size
+        self.n_epochs = n_epochs
+        self.gamma = gamma
+        self.total_timesteps = total_timesteps
+        self.eval_interval = eval_interval
+        self.eval_deterministic = eval_deterministic
+        self.entropy_coef = entropy_coef
+        self.critic_coef = critic_coef
+        self.normalize_advantage = normalize_advantage
+        self.device = device if device else ("cuda" if torch.cuda.is_available() else "cpu")
+        self.eval_episodes = eval_episodes
+
+    def make_env(self, eval=False):
+        """Creates an environment and wraps it if necessary."""
+        env_spec = self.env_spec_eval if eval else self.env_spec
+        if isinstance(env_spec, str):
+            env = gym.make(env_spec)
+            env = GymWrapper(env).to(self.device)
+        elif callable(env_spec):
+            env = env_spec()
+            if isinstance(env, gym.Env):
+                env = GymWrapper(env).to(self.device)
+        elif isinstance(env, gym.Env):
+            env = GymWrapper(env).to(self.device)
+        else:
+            raise ValueError("env_spec must be a string or a callable that returns an environment.")
+        return env
+
+    def train_step(self, batch):
+        raise NotImplementedError("train_step method must be implemented in subclass.")
+
+    def train(self):
+        raise NotImplementedError("train method must be implemented in subclass.")
+
+    def evaluate(self, epoch):
+        raise NotImplementedError("evaluate method must be implemented in subclass.")
+
+def dump_video(module):
+    if isinstance(module, VideoRecorder):
+        module.dump()

fancy_rl/algos/on_policy.py

@@ -5,50 +5,58 @@ from torchrl.data import LazyMemmapStorage, TensorDictReplayBuffer
 from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement
 from torchrl.envs.libs.gym import GymWrapper
 from torchrl.envs import ExplorationType, set_exploration_type
-from torchrl.record import VideoRecorder
-from tensordict import LazyStackedTensorDict, TensorDict
-from abc import ABC
 
 from fancy_rl.loggers import TerminalLogger
+from fancy_rl.algos.algo import Algo
 
-class OnPolicy(ABC):
+class OnPolicy(Algo):
     def __init__(
         self,
         env_spec,
-        loggers,
         optimizers,
-        learning_rate,
-        n_steps,
-        batch_size,
-        n_epochs,
-        gamma,
-        total_timesteps,
-        eval_interval,
-        eval_deterministic,
-        entropy_coef,
-        critic_coef,
-        normalize_advantage,
-        device=None,
-        eval_episodes=10,
+        loggers=None,
+        actor_hidden_sizes=[64, 64],
+        critic_hidden_sizes=[64, 64],
+        actor_activation_fn="Tanh",
+        critic_activation_fn="Tanh",
+        learning_rate=3e-4,
+        n_steps=2048,
+        batch_size=64,
+        n_epochs=10,
+        gamma=0.99,
+        gae_lambda=0.95,
+        total_timesteps=1e6,
+        eval_interval=2048,
+        eval_deterministic=True,
+        entropy_coef=0.01,
+        critic_coef=0.5,
+        normalize_advantage=True,
+        clip_range=0.2,
         env_spec_eval=None,
+        eval_episodes=10,
+        device=None,
     ):
-        self.env_spec = env_spec
-        self.env_spec_eval = env_spec_eval if env_spec_eval is not None else env_spec
-        self.loggers = loggers if loggers != None else [TerminalLogger(None, None)]
-        self.optimizers = optimizers
-        self.learning_rate = learning_rate
-        self.n_steps = n_steps
-        self.batch_size = batch_size
-        self.n_epochs = n_epochs
-        self.gamma = gamma
-        self.total_timesteps = total_timesteps
-        self.eval_interval = eval_interval
-        self.eval_deterministic = eval_deterministic
-        self.entropy_coef = entropy_coef
-        self.critic_coef = critic_coef
-        self.normalize_advantage = normalize_advantage
-        self.device = device if device else ("cuda" if torch.cuda.is_available() else "cpu")
-        self.eval_episodes = eval_episodes
+        device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        super().__init__(
+            env_spec=env_spec,
+            loggers=loggers,
+            optimizers=optimizers,
+            learning_rate=learning_rate,
+            n_steps=n_steps,
+            batch_size=batch_size,
+            n_epochs=n_epochs,
+            gamma=gamma,
+            total_timesteps=total_timesteps,
+            eval_interval=eval_interval,
+            eval_deterministic=eval_deterministic,
+            entropy_coef=entropy_coef,
+            critic_coef=critic_coef,
+            normalize_advantage=normalize_advantage,
+            device=device,
+            env_spec_eval=env_spec_eval,
+            eval_episodes=eval_episodes,
+        )
 
         # Create collector
         self.collector = SyncDataCollector(
@@ -69,21 +77,6 @@ class OnPolicy(ABC):
             batch_size=self.batch_size,
         )
 
-    def make_env(self, eval=False):
-        """Creates an environment and wraps it if necessary."""
-        env_spec = self.env_spec_eval if eval else self.env_spec
-        if isinstance(env_spec, str):
-            env = gym.make(env_spec)
-            env = GymWrapper(env).to(self.device)
-        elif callable(env_spec):
-            env = env_spec()
-            if isinstance(env, gym.Env):
-                env = GymWrapper(env).to(self.device)
-        elif isinstance(env, gym.Env):
-            env = GymWrapper(env).to(self.device)
-        else:
-            raise ValueError("env_spec must be a string or a callable that returns an environment.")
-        return env
 
     def train_step(self, batch):
         for optimizer in self.optimizers.values():
@@ -140,7 +133,3 @@ class OnPolicy(ABC):
         avg_return = torch.cat(test_rewards, 0).mean().item()
         for logger in self.loggers:
             logger.log_scalar({"eval_avg_return": avg_return}, step=epoch)
-
-def dump_video(module):
-    if isinstance(module, VideoRecorder):
-        module.dump()