diff --git a/sota-implementations/ppo/ppo_atari.py b/sota-implementations/ppo/ppo_atari.py
index 6d8883393d5..276c706baef 100644
--- a/sota-implementations/ppo/ppo_atari.py
+++ b/sota-implementations/ppo/ppo_atari.py
@@ -5,7 +5,7 @@
 
 """
 This script reproduces the Proximal Policy Optimization (PPO) Algorithm
-results from Schulman et al. 2017 for the on Atari Environments.
+results from Schulman et al. 2017 for the Atari Environments.
 """
 import hydra
 from torchrl._utils import logger as torchrl_logger
diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py
index 22aec1cbb0d..1c473d31297 100644
--- a/torchrl/envs/common.py
+++ b/torchrl/envs/common.py
@@ -516,7 +516,7 @@ def append_transform(
         self,
         transform: "Transform"  # noqa: F821
         | Callable[[TensorDictBase], TensorDictBase],
-    ) -> None:
+    ) -> EnvBase:
         """Returns a transformed environment where the callable/transform passed is applied.
 
         Args:
@@ -1482,7 +1482,8 @@ def full_state_spec(self, spec: Composite) -> None:
 
     # Single-env specs can be used to remove the batch size from the spec
     @property
-    def batch_dims(self):
+    def batch_dims(self) -> int:
+        """Number of batch dimensions of the env."""
         return len(self.batch_size)
 
     def _make_single_env_spec(self, spec: TensorSpec) -> TensorSpec:
@@ -2444,11 +2445,11 @@ def rollout(
         set_truncated: bool = False,
         out=None,
         trust_policy: bool = False,
-    ):
+    ) -> TensorDictBase:
         """Executes a rollout in the environment.
 
-        The function will stop as soon as one of the contained environments
-        returns done=True.
+        The function will return as soon as any of the contained environments
+        reaches any of the done states.
 
         Args:
             max_steps (int): maximum number of steps to be executed. The actual number of steps can be smaller if
@@ -2464,14 +2465,16 @@ def rollout(
                 the call to ``rollout``.
 
         Keyword Args:
-            auto_reset (bool, optional): if ``True``, resets automatically the environment
-                if it is in a done state when the rollout is initiated.
-                Default is ``True``.
+            auto_reset (bool, optional): if ``True``, the contained environments will be reset before starting the
+                rollout. If ``False``, then the rollout will continue from a previous state, which requires the
+                ``tensordict`` argument to be passed with the previous rollout. Default is ``True``.
             auto_cast_to_device (bool, optional): if ``True``, the device of the tensordict is automatically cast to the
                 policy device before the policy is used. Default is ``False``.
-            break_when_any_done (bool): breaks if any of the done state is True. If False, a reset() is
-                called on the sub-envs that are done. Default is True.
-            break_when_all_done (bool): TODO
+            break_when_any_done (bool): if ``True``, break when any of the contained environments reaches any of the
+                done states. If ``False``, then the done environments are reset automatically. Default is ``True``.
+            break_when_all_done (bool, optional): if ``True``, break if all of the contained environments reach any
+                of the done states. If ``False``, break if at least one environment reaches any of the done states.
+                Default is ``False``.
             return_contiguous (bool): if False, a LazyStackedTensorDict will be returned. Default is True.
             tensordict (TensorDict, optional): if ``auto_reset`` is False, an initial
                 tensordict must be provided. Rollout will check if this tensordict has done flags and reset the