Rename _dump_logs() and update changelog

docs/guide/callbacks.rst

@@ -143,6 +143,7 @@ Stable Baselines provides you with a set of common callbacks for:
 - evaluating the model periodically and saving the best one (:ref:`EvalCallback`)
 - chaining callbacks (:ref:`CallbackList`)
 - triggering callback on events (:ref:`EventCallback`, :ref:`EveryNTimesteps`)
+- logging data every N timesteps (:ref:`LogEveryNTimesteps`)
 - stopping the training early based on a reward threshold (:ref:`StopTrainingOnRewardThreshold <StopTrainingCallback>`)
 
 
@@ -313,7 +314,7 @@ An :ref:`EventCallback` that will trigger its child callback every ``n_steps`` t
 
 .. note::
 
-	Because of the way ``PPO1`` and ``TRPO`` work (they rely on MPI), ``n_steps`` is a lower bound between two events.
+	Because of the way ``VecEnv`` work, ``n_steps`` is a lower bound between two events when using multiple environments.
 
 
 .. code-block:: python
@@ -330,7 +331,30 @@ An :ref:`EventCallback` that will trigger its child callback every ``n_steps`` t
 
   model = PPO("MlpPolicy", "Pendulum-v1", verbose=1)
 
-  model.learn(int(2e4), callback=event_callback)
+  model.learn(20_000, callback=event_callback)
+
+.. _LogEveryNTimesteps:
+
+LogEveryNTimesteps
+^^^^^^^^^^^^^^^^^^
+
+A callback derived from :ref:`EveryNTimesteps` that will dump the logged data every ``n_steps`` timesteps.
+
+
+.. code-block:: python
+
+  import gymnasium as gym
+
+  from stable_baselines3 import PPO
+  from stable_baselines3.common.callbacks import LogEveryNTimesteps
+
+  event_callback = LogEveryNTimesteps(n_steps=1_000)
+
+  model = PPO("MlpPolicy", "Pendulum-v1", verbose=1)
+
+  # Disable auto-logging by passing `log_interval=None`
+  model.learn(10_000, callback=event_callback, log_interval=None)
+
 
 
 .. _StopTrainingOnMaxEpisodes:

docs/misc/changelog.rst

@@ -3,7 +3,7 @@
 Changelog
 ==========
 
-Release 2.6.0a0 (WIP)
+Release 2.6.0a1 (WIP)
 --------------------------
 
 
@@ -13,6 +13,7 @@ Breaking Changes:
 New Features:
 ^^^^^^^^^^^^^
 - Added ``has_attr`` method for ``VecEnv`` to check if an attribute exists
+- Added ``LogEveryNTimesteps`` callback to dump logs every N timesteps (note: you need to pass ``log_interval=None`` to avoid any interference)
 
 Bug Fixes:
 ^^^^^^^^^^
@@ -29,6 +30,7 @@ Bug Fixes:
 
 Deprecations:
 ^^^^^^^^^^^^^
+- ``algo._dump_logs()`` is deprecated in favor of ``algo.dump_logs()`` and will be removed in SB3 v2.7.0
 
 Others:
 ^^^^^^^

stable_baselines3/common/base_class.py

@@ -866,8 +866,12 @@ def save(
 
         save_to_zip_file(path, data=data, params=params_to_save, pytorch_variables=pytorch_variables)
 
-    @abstractmethod
-    def _dump_logs(self) -> None:
+    def dump_logs(self) -> None:
         """
         Write log data. (Implemented by OffPolicyAlgorithm and OnPolicyAlgorithm)
         """
+        raise NotImplementedError()
+
+    def _dump_logs(self, *args) -> None:
+        warnings.warn("algo._dump_logs() is deprecated in favor of algo.dump_logs(). It will be removed in SB3 v2.7.0")
+        self.dump_logs(*args)

stable_baselines3/common/callbacks.py

@@ -602,7 +602,7 @@ def __init__(self, n_steps: int):
         super().__init__(n_steps, callback=ConvertCallback(self._log_data))
 
     def _log_data(self, _locals: dict[str, Any], _globals: dict[str, Any]) -> bool:
-        self.model._dump_logs()
+        self.model.dump_logs()
         return True
 
 

stable_baselines3/common/off_policy_algorithm.py

@@ -406,7 +406,7 @@ def _sample_action(
             action = buffer_action
         return action, buffer_action
 
-    def _dump_logs(self) -> None:
+    def dump_logs(self) -> None:
         """
         Write log data.
         """
@@ -594,7 +594,7 @@ def collect_rollouts(
 
                     # Log training infos
                     if log_interval is not None and self._episode_num % log_interval == 0:
-                        self._dump_logs()
+                        self.dump_logs()
         callback.on_rollout_end()
 
         return RolloutReturn(num_collected_steps * env.num_envs, num_collected_episodes, continue_training)

stable_baselines3/common/on_policy_algorithm.py

@@ -274,7 +274,7 @@ def train(self) -> None:
         """
         raise NotImplementedError
 
-    def _dump_logs(self, iteration: int = 0) -> None:
+    def dump_logs(self, iteration: int = 0) -> None:
         """
         Write log.
 
@@ -332,7 +332,7 @@ def learn(
             # Display training infos
             if log_interval is not None and iteration % log_interval == 0:
                 assert self.ep_info_buffer is not None
-                self._dump_logs(iteration)
+                self.dump_logs(iteration)
 
             self.train()
 

stable_baselines3/version.txt

@@ -1 +1 @@
-2.6.0a0
+2.6.0a1