Update QRDQN defaults (#225)
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Changelog
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==========
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Release 2.3.0a1 (WIP)
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--------------------------
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Breaking Changes:
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^^^^^^^^^^^^^^^^^
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- Upgraded to Stable-Baselines3 >= 2.3.0
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- The default ``leanrning_starts`` parameter of ``QRDQN`` have been changed to be consistent with the other offpolicy algorithms
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.. code-block:: python
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# SB3 < 2.3.0 default hyperparameters, 50_000 corresponded to Atari defaults hyperparameters
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# model = QRDQN("MlpPolicy", env, learning_start=50_000)
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# SB3 >= 2.3.0:
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model = QRDQN("MlpPolicy", env, learning_start=100)
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New Features:
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^^^^^^^^^^^^^
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Bug Fixes:
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^^^^^^^^^^
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Deprecations:
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^^^^^^^^^^^^^
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Others:
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^^^^^^^
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Documentation:
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^^^^^^^^^^^^^^
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Release 2.2.1 (2023-11-17)
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--------------------------
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@ -20,7 +20,8 @@ class QRDQN(OffPolicyAlgorithm):
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"""
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Quantile Regression Deep Q-Network (QR-DQN)
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Paper: https://arxiv.org/abs/1710.10044
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Default hyperparameters are taken from the paper and are tuned for Atari games.
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Default hyperparameters are taken from the paper and are tuned for Atari games
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(except for the ``learning_starts`` parameter).
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:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: The environment to learn from (if registered in Gym, can be str)
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@ -77,7 +78,7 @@ class QRDQN(OffPolicyAlgorithm):
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env: Union[GymEnv, str],
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learning_rate: Union[float, Schedule] = 5e-5,
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buffer_size: int = 1000000, # 1e6
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learning_starts: int = 50000,
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learning_starts: int = 100,
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batch_size: int = 32,
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tau: float = 1.0,
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gamma: float = 0.99,
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@ -1 +1 @@
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2.2.1
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2.3.0a1
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2
setup.py
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setup.py
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@ -65,7 +65,7 @@ setup(
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packages=[package for package in find_packages() if package.startswith("sb3_contrib")],
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package_data={"sb3_contrib": ["py.typed", "version.txt"]},
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install_requires=[
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"stable_baselines3>=2.2.1,<3.0",
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"stable_baselines3>=2.3.0a0,<3.0",
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],
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description="Contrib package of Stable Baselines3, experimental code.",
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author="Antonin Raffin",
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