Fixing #1791

docs/misc/changelog.rst

@@ -17,6 +17,7 @@ Bug Fixes:
 - Fixed memory leak when loading learner from storage, ``set_parameters()`` does not try to load the object data anymore
   and only loads the PyTorch parameters (@peteole)
 - Cast type in compute gae method to avoid error when using torch compile (@amjames)
+- `CallbackList` now sets the `.parent` attribute of child callbacks to its own `.parent`. This resolves https://github.com/DLR-RM/stable-baselines3/issues/1791 (will-maclean)
 
 `SB3-Contrib`_
 ^^^^^^^^^^^^^^
@@ -1661,4 +1662,4 @@ And all the contributors:
 @anand-bala @hughperkins @sidney-tio @AlexPasqua @dominicgkerr @Akhilez @Rocamonde @tobirohrer @ZikangXiong @ReHoss
 @DavyMorgan @luizapozzobon @Bonifatius94 @theSquaredError @harveybellini @DavyMorgan @FieteO @jonasreiher @npit @WeberSamuel @troiganto
 @lutogniew @lbergmann1 @lukashass @BertrandDecoster @pseudo-rnd-thoughts @stefanbschneider @kyle-he @PatrickHelm @corentinlger
-@marekm4 @stagoverflow @rushitnshah @markscsmith @NickLucche @cschindlbeck @peteole
+@marekm4 @stagoverflow @rushitnshah @markscsmith @NickLucche @cschindlbeck @peteole @will-maclean

stable_baselines3/common/callbacks.py

@@ -204,6 +204,10 @@ def _init_callback(self) -> None:
         for callback in self.callbacks:
             callback.init_callback(self.model)
 
+            # Fix for https://github.com/DLR-RM/stable-baselines3/issues/1791
+            # pass through the parent callback to all children
+            callback.parent = self.parent
+
     def _on_training_start(self) -> None:
         for callback in self.callbacks:
             callback.on_training_start(self.locals, self.globals)

tests/test_callbacks.py

@@ -264,3 +264,31 @@ def test_checkpoint_additional_info(tmp_path):
     model = DQN.load(checkpoint_dir / "rl_model_200_steps.zip")
     model.load_replay_buffer(checkpoint_dir / "rl_model_replay_buffer_200_steps.pkl")
     VecNormalize.load(checkpoint_dir / "rl_model_vecnormalize_200_steps.pkl", dummy_vec_env)
+
+
+def test_eval_callback_chaining():
+    class CustomCallback(BaseCallback):
+
+        def __init__(self, verbose=0):
+            super().__init__(verbose)
+
+        def _on_step(self):
+            # Draw a figure
+            return True
+
+    eval_env = gym.make("Pendulum-v1")
+
+    stop_on_threshold_callback = StopTrainingOnRewardThreshold(reward_threshold=-200, verbose=1)
+
+    eval_callback = EvalCallback(
+        eval_env,
+        best_model_save_path="./logs/",
+        log_path="./logs/",
+        eval_freq=199,
+        deterministic=True,
+        render=False,
+        callback_on_new_best=CallbackList([CustomCallback(), stop_on_threshold_callback]),
+    )
+
+    model = PPO("MlpPolicy", "Pendulum-v1")
+    model.learn(200, callback=eval_callback)