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a89d35f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import pytest
import torch as th
from stable_baselines3 import A2C, DQN, PPO, SAC, TD3
from stable_baselines3.common.sb2_compat.rmsprop_tf_like import RMSpropTFLike
@pytest.mark.parametrize(
"net_arch",
[
[12, dict(vf=[16], pi=[8])],
[4],
[],
[4, 4],
[12, dict(vf=[8, 4], pi=[8])],
[12, dict(vf=[8], pi=[8, 4])],
[12, dict(pi=[8])],
],
)
@pytest.mark.parametrize("model_class", [A2C, PPO])
def test_flexible_mlp(model_class, net_arch):
_ = model_class("MlpPolicy", "CartPole-v1", policy_kwargs=dict(net_arch=net_arch), n_steps=100).learn(300)
@pytest.mark.parametrize("net_arch", [[], [4], [4, 4], dict(qf=[8], pi=[8, 4])])
@pytest.mark.parametrize("model_class", [SAC, TD3])
def test_custom_offpolicy(model_class, net_arch):
_ = model_class("MlpPolicy", "Pendulum-v0", policy_kwargs=dict(net_arch=net_arch), learning_starts=100).learn(300)
@pytest.mark.parametrize("model_class", [A2C, PPO, SAC, TD3])
@pytest.mark.parametrize("optimizer_kwargs", [None, dict(weight_decay=0.0)])
def test_custom_optimizer(model_class, optimizer_kwargs):
kwargs = {}
if model_class in {DQN, SAC, TD3}:
kwargs = dict(learning_starts=100)
elif model_class in {A2C, PPO}:
kwargs = dict(n_steps=100)
policy_kwargs = dict(optimizer_class=th.optim.AdamW, optimizer_kwargs=optimizer_kwargs, net_arch=[32])
_ = model_class("MlpPolicy", "Pendulum-v0", policy_kwargs=policy_kwargs, **kwargs).learn(300)
def test_tf_like_rmsprop_optimizer():
policy_kwargs = dict(optimizer_class=RMSpropTFLike, net_arch=[32])
_ = A2C("MlpPolicy", "Pendulum-v0", policy_kwargs=policy_kwargs).learn(500)
def test_dqn_custom_policy():
policy_kwargs = dict(optimizer_class=RMSpropTFLike, net_arch=[32])
_ = DQN("MlpPolicy", "CartPole-v1", policy_kwargs=policy_kwargs, learning_starts=100).learn(300)
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