mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-06-26 03:01:19 +00:00
2cd6a4f93b
* Fix storing correct episode dones * Fix number of filters in NatureCNN network * Add TF-like RMSprop for matching performance with sb2 * Remove stuff that was accidentally included * Reformat * Clarify variable naming * Update changelog * Add comment on RMSprop implementations to A2C * Add test for RMSpropTFLike Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
import pytest
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import torch as th
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from stable_baselines3 import A2C, PPO, SAC, TD3
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from stable_baselines3.common.sb2_compat.rmsprop_tf_like import RMSpropTFLike
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@pytest.mark.parametrize(
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"net_arch",
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[
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[12, dict(vf=[16], pi=[8])],
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[4],
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[],
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[4, 4],
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[12, dict(vf=[8, 4], pi=[8])],
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[12, dict(vf=[8], pi=[8, 4])],
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[12, dict(pi=[8])],
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],
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)
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@pytest.mark.parametrize("model_class", [A2C, PPO])
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def test_flexible_mlp(model_class, net_arch):
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_ = model_class("MlpPolicy", "CartPole-v1", policy_kwargs=dict(net_arch=net_arch), n_steps=100).learn(1000)
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@pytest.mark.parametrize("net_arch", [[4], [4, 4],])
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@pytest.mark.parametrize("model_class", [SAC, TD3])
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def test_custom_offpolicy(model_class, net_arch):
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_ = model_class("MlpPolicy", "Pendulum-v0", policy_kwargs=dict(net_arch=net_arch)).learn(1000)
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@pytest.mark.parametrize("model_class", [A2C, PPO, SAC, TD3])
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@pytest.mark.parametrize("optimizer_kwargs", [None, dict(weight_decay=0.0)])
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def test_custom_optimizer(model_class, optimizer_kwargs):
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policy_kwargs = dict(optimizer_class=th.optim.AdamW, optimizer_kwargs=optimizer_kwargs, net_arch=[32])
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_ = model_class("MlpPolicy", "Pendulum-v0", policy_kwargs=policy_kwargs).learn(1000)
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def test_tf_like_rmsprop_optimizer():
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policy_kwargs = dict(optimizer_class=RMSpropTFLike, net_arch=[32])
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_ = A2C("MlpPolicy", "Pendulum-v0", policy_kwargs=policy_kwargs).learn(1000)
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