111 lines
2.8 KiB
Python
111 lines
2.8 KiB
Python
import gym
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import pytest
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from stable_baselines3.common.env_util import make_vec_env
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from sb3_contrib import QRDQN, TQC, TRPO
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@pytest.mark.parametrize("ent_coef", ["auto", 0.01, "auto_0.01"])
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def test_tqc(ent_coef):
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model = TQC(
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"MlpPolicy",
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"Pendulum-v0",
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policy_kwargs=dict(net_arch=[64, 64]),
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learning_starts=100,
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verbose=1,
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create_eval_env=True,
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ent_coef=ent_coef,
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)
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model.learn(total_timesteps=300, eval_freq=250)
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@pytest.mark.parametrize("n_critics", [1, 3])
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def test_n_critics(n_critics):
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# Test TQC with different number of critics
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model = TQC(
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"MlpPolicy",
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"Pendulum-v0",
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policy_kwargs=dict(net_arch=[64], n_critics=n_critics),
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learning_starts=100,
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verbose=1,
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)
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model.learn(total_timesteps=300)
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def test_sde():
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model = TQC(
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"MlpPolicy",
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"Pendulum-v0",
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policy_kwargs=dict(net_arch=[64]),
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use_sde=True,
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learning_starts=100,
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verbose=1,
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)
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model.learn(total_timesteps=300)
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model.policy.reset_noise()
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model.policy.actor.get_std()
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def test_qrdqn():
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model = QRDQN(
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"MlpPolicy",
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"CartPole-v1",
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policy_kwargs=dict(n_quantiles=25, net_arch=[64, 64]),
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learning_starts=100,
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buffer_size=500,
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learning_rate=3e-4,
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verbose=1,
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create_eval_env=True,
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)
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model.learn(total_timesteps=500, eval_freq=250)
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@pytest.mark.parametrize("env_id", ["CartPole-v1", "Pendulum-v0"])
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def test_trpo(env_id):
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model = TRPO("MlpPolicy", env_id, n_steps=128, seed=0, policy_kwargs=dict(net_arch=[16]), verbose=1)
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model.learn(total_timesteps=500)
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def test_trpo_params():
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# Test with gSDE and subsampling
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model = TRPO(
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"MlpPolicy",
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"Pendulum-v0",
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n_steps=64,
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batch_size=32,
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use_sde=True,
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sub_sampling_factor=4,
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seed=0,
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policy_kwargs=dict(net_arch=[dict(pi=[32], vf=[32])]),
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verbose=1,
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)
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model.learn(total_timesteps=500)
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@pytest.mark.parametrize("model_class", [TQC, QRDQN])
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def test_offpolicy_multi_env(model_class):
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if model_class in [TQC]:
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env_id = "Pendulum-v0"
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policy_kwargs = dict(net_arch=[64], n_critics=1)
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else:
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env_id = "CartPole-v1"
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policy_kwargs = dict(net_arch=[64])
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def make_env():
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env = gym.make(env_id)
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# to check that the code handling timeouts runs
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env = gym.wrappers.TimeLimit(env, 50)
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return env
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env = make_vec_env(make_env, n_envs=2)
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model = model_class(
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"MlpPolicy",
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env,
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policy_kwargs=policy_kwargs,
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learning_starts=100,
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buffer_size=10000,
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verbose=0,
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train_freq=5,
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)
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model.learn(total_timesteps=150)
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