Update to new logger (#32)

docs/misc/changelog.rst

@@ -3,14 +3,15 @@
 Changelog
 ==========
 
-Release 1.1.0a7 (WIP)
+Release 1.1.0a11 (WIP)
 -------------------------------
 
 Breaking Changes:
 ^^^^^^^^^^^^^^^^^
-- Added support for Dictionary observation spaces (cf SB3 doc)
+- Added support for Dictionary observation spaces (cf. SB3 doc)
 - Upgraded to Stable-Baselines3 >= 1.1.0
-- Added proper handling of timeouts for off-policy algorithms (cf SB3 doc)
+- Added proper handling of timeouts for off-policy algorithms (cf. SB3 doc)
+- Updated usage of logger (cf. SB3 doc)
 
 Bug Fixes:
 ^^^^^^^^^^

sb3_contrib/qrdqn/qrdqn.py

@@ -3,7 +3,6 @@ from typing import Any, Dict, List, Optional, Tuple, Type, Union
 import gym
 import numpy as np
 import torch as th
-from stable_baselines3.common import logger
 from stable_baselines3.common.buffers import ReplayBuffer
 from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
 from stable_baselines3.common.preprocessing import maybe_transpose
@@ -153,7 +152,7 @@ class QRDQN(OffPolicyAlgorithm):
             polyak_update(self.quantile_net.parameters(), self.quantile_net_target.parameters(), self.tau)
 
         self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)
-        logger.record("rollout/exploration rate", self.exploration_rate)
+        self.logger.record("rollout/exploration rate", self.exploration_rate)
 
     def train(self, gradient_steps: int, batch_size: int = 100) -> None:
         # Update learning rate according to schedule
@@ -199,8 +198,8 @@ class QRDQN(OffPolicyAlgorithm):
         # Increase update counter
         self._n_updates += gradient_steps
 
-        logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
-        logger.record("train/loss", np.mean(losses))
+        self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
+        self.logger.record("train/loss", np.mean(losses))
 
     def predict(
         self,

sb3_contrib/tqc/tqc.py

@@ -3,7 +3,6 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
 import gym
 import numpy as np
 import torch as th
-from stable_baselines3.common import logger
 from stable_baselines3.common.buffers import ReplayBuffer
 from stable_baselines3.common.noise import ActionNoise
 from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
@@ -265,12 +264,12 @@ class TQC(OffPolicyAlgorithm):
 
         self._n_updates += gradient_steps
 
-        logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
-        logger.record("train/ent_coef", np.mean(ent_coefs))
-        logger.record("train/actor_loss", np.mean(actor_losses))
-        logger.record("train/critic_loss", np.mean(critic_losses))
+        self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
+        self.logger.record("train/ent_coef", np.mean(ent_coefs))
+        self.logger.record("train/actor_loss", np.mean(actor_losses))
+        self.logger.record("train/critic_loss", np.mean(critic_losses))
         if len(ent_coef_losses) > 0:
-            logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
+            self.logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
 
     def learn(
         self,

sb3_contrib/version.txt

@@ -1 +1 @@
-1.1.0a7
+1.1.0a11

setup.py

@@ -62,7 +62,7 @@ setup(
     packages=[package for package in find_packages() if package.startswith("sb3_contrib")],
     package_data={"sb3_contrib": ["py.typed", "version.txt"]},
     install_requires=[
-        "stable_baselines3>=1.1.0a7",
+        "stable_baselines3>=1.1.0a11",
     ],
     description="Contrib package of Stable Baselines3, experimental code.",
     author="Antonin Raffin",