diff --git a/tianshou/data/collector.py b/tianshou/data/collector.py
index b498f45..d2ca217 100644
--- a/tianshou/data/collector.py
+++ b/tianshou/data/collector.py
@@ -237,7 +237,10 @@ class BaseCollector(ABC):
         self,
         gym_reset_kwargs: dict[str, Any] | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
-        """Reset the environments and the initial obs, info, and hidden state of the collector."""
+        """Reset the environments and the initial obs, info, and hidden state of the collector.
+
+        :return: The initial observation and info from the (vectorized) environment.
+        """
         gym_reset_kwargs = gym_reset_kwargs or {}
         obs_NO, info_N = self.env.reset(**gym_reset_kwargs)
         # TODO: hack, wrap envpool envs such that they don't return a dict
@@ -368,16 +371,17 @@ class Collector(BaseCollector):
         self._is_closed = False
         self.collect_step, self.collect_episode, self.collect_time = 0, 0, 0.0
 
+    @override
     def close(self) -> None:
         super().close()
         self._pre_collect_obs_RO = None
         self._pre_collect_info_R = None
 
+    @override
     def reset_env(
         self,
         gym_reset_kwargs: dict[str, Any] | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
-        """Reset the environments and the initial obs, info, and hidden state of the collector."""
         obs_NO, info_N = super().reset_env(gym_reset_kwargs=gym_reset_kwargs)
         # We assume that R = N when reset is called.
         # TODO: there is currently no mechanism that ensures this and it's a public method!
@@ -457,6 +461,8 @@ class Collector(BaseCollector):
             ready_env_ids_R = np.arange(self.env_num)
         elif n_episode is not None:
             ready_env_ids_R = np.arange(min(self.env_num, n_episode))
+        else:
+            raise ValueError("Either n_step or n_episode should be set.")
 
         start_time = time.time()
         if self._pre_collect_obs_RO is None or self._pre_collect_info_R is None:
@@ -645,8 +651,8 @@ class Collector(BaseCollector):
             collect_speed=step_count / collect_time,
         )
 
+    @staticmethod
     def _reset_hidden_state_based_on_type(
-        self,
         env_ind_local_D: np.ndarray,
         last_hidden_state_RH: np.ndarray | torch.Tensor | Batch | None,
     ) -> None:
@@ -700,21 +706,13 @@ class AsyncCollector(Collector):
         self._current_action_in_all_envs_EA: np.ndarray = np.empty(self.env_num)
         self._current_policy_in_all_envs_E: Batch | None = None
 
+    @override
     def reset(
         self,
         reset_buffer: bool = True,
         reset_stats: bool = True,
         gym_reset_kwargs: dict[str, Any] | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
-        """Reset the environment, statistics, and data needed to start the collection.
-
-        :param reset_buffer: if true, reset the replay buffer attached
-            to the collector.
-        :param reset_stats: if true, reset the statistics attached to the collector.
-        :param gym_reset_kwargs: extra keyword arguments to pass into the environment's
-            reset function. Defaults to None (extra keyword arguments)
-        :return: The initial observation and info from the environment.
-        """
         # This sets the _pre_collect attrs
         result = super().reset(
             reset_buffer=reset_buffer,