Fix replay_buffer_class type annotation (#1042)

* Fix replay_buffer_class type annotation

* Update changelog

* Further replacement of same type annotation issue

* Formatting

* Rolled back formatting changes for consistency

docs/misc/changelog.rst

@@ -27,6 +27,7 @@ Bug Fixes:
 - Added multidimensional action space support (@qgallouedec)
 - Fixed missing verbose parameter passing in the ``EvalCallback`` constructor (@burakdmb)
 - Fixed the issue that when updating the target network in DQN, SAC, TD3, the ``running_mean`` and ``running_var`` properties of batch norm layers are not updated (@honglu2875)
+- Fixed incorrect type annotation of the replay_buffer_class argument in ``common.OffPolicyAlgorithm`` initializer, where an instance instead of a class was required (@Rocamonde)
 
 Deprecations:
 ^^^^^^^^^^^^^

stable_baselines3/common/off_policy_algorithm.py

@@ -85,7 +85,7 @@ class OffPolicyAlgorithm(BaseAlgorithm):
         train_freq: Union[int, Tuple[int, str]] = (1, "step"),
         gradient_steps: int = 1,
         action_noise: Optional[ActionNoise] = None,
-        replay_buffer_class: Optional[ReplayBuffer] = None,
+        replay_buffer_class: Optional[Type[ReplayBuffer]] = None,
         replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
         optimize_memory_usage: bool = False,
         policy_kwargs: Optional[Dict[str, Any]] = None,

stable_baselines3/ddpg/ddpg.py

@@ -66,7 +66,7 @@ class DDPG(TD3):
         train_freq: Union[int, Tuple[int, str]] = (1, "episode"),
         gradient_steps: int = -1,
         action_noise: Optional[ActionNoise] = None,
-        replay_buffer_class: Optional[ReplayBuffer] = None,
+        replay_buffer_class: Optional[Type[ReplayBuffer]] = None,
         replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
         optimize_memory_usage: bool = False,
         tensorboard_log: Optional[str] = None,

stable_baselines3/dqn/dqn.py

@@ -78,7 +78,7 @@ class DQN(OffPolicyAlgorithm):
         gamma: float = 0.99,
         train_freq: Union[int, Tuple[int, str]] = 4,
         gradient_steps: int = 1,
-        replay_buffer_class: Optional[ReplayBuffer] = None,
+        replay_buffer_class: Optional[Type[ReplayBuffer]] = None,
         replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
         optimize_memory_usage: bool = False,
         target_update_interval: int = 10000,

stable_baselines3/sac/sac.py

@@ -92,7 +92,7 @@ class SAC(OffPolicyAlgorithm):
         train_freq: Union[int, Tuple[int, str]] = 1,
         gradient_steps: int = 1,
         action_noise: Optional[ActionNoise] = None,
-        replay_buffer_class: Optional[ReplayBuffer] = None,
+        replay_buffer_class: Optional[Type[ReplayBuffer]] = None,
         replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
         optimize_memory_usage: bool = False,
         ent_coef: Union[str, float] = "auto",

stable_baselines3/td3/td3.py

@@ -80,7 +80,7 @@ class TD3(OffPolicyAlgorithm):
         train_freq: Union[int, Tuple[int, str]] = (1, "episode"),
         gradient_steps: int = -1,
         action_noise: Optional[ActionNoise] = None,
-        replay_buffer_class: Optional[ReplayBuffer] = None,
+        replay_buffer_class: Optional[Type[ReplayBuffer]] = None,
         replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
         optimize_memory_usage: bool = False,
         policy_delay: int = 2,