Fix: Issue with env wrapping (ensure batch dim)

fancy_rl/algos/algo.py

@@ -1,9 +1,12 @@
 import torch
 import gymnasium as gym
-from torchrl.envs.libs.gym import GymWrapper
+from torchrl.envs import GymEnv, TransformedEnv, Compose, RewardSum, StepCounter, ParallelEnv
 from torchrl.record import VideoRecorder
 from abc import ABC
+import pdb
 from tensordict import TensorDict
+from torchrl.envs import GymWrapper, TransformedEnv
+from torchrl.envs import BatchSizeTransform
 
 from fancy_rl.loggers import TerminalLogger
 
@@ -47,18 +50,33 @@ class Algo(ABC):
         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
+        env = self._wrap_env(env_spec)
+        env.reset()
+        return env
+
+    def _wrap_env(self, env_spec):
         if isinstance(env_spec, str):
-            env = gym.make(env_spec)
-            env = GymWrapper(env).to(self.device)
+            env = GymEnv(env_spec, device=self.device)
+        elif isinstance(env_spec, gym.Env):
+            env = GymWrapper(env_spec, device=self.device)
+        elif isinstance(env_spec, GymEnv):
+            env = env_spec
         elif callable(env_spec):
-            env = env_spec()
-            if not (isinstance(env, gym.Env) or isinstance(env, gym.core.Wrapper)):
-                raise ValueError("env_spec must be a string or a callable that returns an environment. Was a callable that returned a {}".format(type(env)))
-            env = GymWrapper(env).to(self.device)
+            base_env = env_spec()
+            return self._wrap_env(base_env)
         else:
-            raise ValueError("env_spec must be a string or a callable that returns an environment. Was a {}".format(type(env_spec)))
+            raise ValueError(
+                f"env_spec must be a string, callable, Gymnasium environment, or GymEnv, "
+                f"got {type(env_spec)}"
+            )
+        
+        if not env.batch_size:
+            env = TransformedEnv(
+                env,
+                BatchSizeTransform(batch_size=torch.Size([1]))
+            )
+        
         return env
 
     def train_step(self, batch):
@@ -78,7 +96,7 @@ class Algo(ABC):
     ):
         with torch.no_grad():
             obs_tensor = torch.as_tensor(observation, device=self.device).unsqueeze(0)
-            td = TensorDict({"observation": obs_tensor}, batch_size=[1])
+            td = TensorDict({"observation": obs_tensor})
             
             action_td = self.prob_actor(td)
             action = action_td["action"]