mirror of
https://github.com/saymrwulf/stable-baselines3.git
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d228364ccf
* Add timeout handling for on-policy algorithms * Fixes * Fix infinite loop in eval * Skip type check for python 3.9 * Fix for discrete obs + add docstring * Fix A2C test * Removed unused helper * Add test for infinite horizon * typed ast should be fixed * Apply suggestions from code review Co-authored-by: Anssi <kaneran21@hotmail.com> Co-authored-by: Anssi <kaneran21@hotmail.com>
51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
import numpy as np
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import pytest
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from stable_baselines3 import A2C, DDPG, DQN, PPO, SAC, TD3
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from stable_baselines3.common.envs import IdentityEnv, IdentityEnvBox, IdentityEnvMultiBinary, IdentityEnvMultiDiscrete
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from stable_baselines3.common.evaluation import evaluate_policy
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from stable_baselines3.common.noise import NormalActionNoise
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from stable_baselines3.common.vec_env import DummyVecEnv
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DIM = 4
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@pytest.mark.parametrize("model_class", [A2C, PPO, DQN])
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@pytest.mark.parametrize("env", [IdentityEnv(DIM), IdentityEnvMultiDiscrete(DIM), IdentityEnvMultiBinary(DIM)])
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def test_discrete(model_class, env):
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env_ = DummyVecEnv([lambda: env])
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kwargs = {}
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n_steps = 3000
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if model_class == DQN:
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kwargs = dict(learning_starts=0)
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n_steps = 4000
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# DQN only support discrete actions
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if isinstance(env, (IdentityEnvMultiDiscrete, IdentityEnvMultiBinary)):
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return
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elif model_class == A2C:
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# slightly higher budget
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n_steps = 3500
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model = model_class("MlpPolicy", env_, gamma=0.4, seed=1, **kwargs).learn(n_steps)
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evaluate_policy(model, env_, n_eval_episodes=20, reward_threshold=90, warn=False)
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obs = env.reset()
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assert np.shape(model.predict(obs)[0]) == np.shape(obs)
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@pytest.mark.parametrize("model_class", [A2C, PPO, SAC, DDPG, TD3])
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def test_continuous(model_class):
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env = IdentityEnvBox(eps=0.5)
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n_steps = {A2C: 3500, PPO: 3000, SAC: 700, TD3: 500, DDPG: 500}[model_class]
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kwargs = dict(policy_kwargs=dict(net_arch=[64, 64]), seed=0, gamma=0.95)
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if model_class in [TD3]:
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n_actions = 1
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action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions))
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kwargs["action_noise"] = action_noise
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model = model_class("MlpPolicy", env, **kwargs).learn(n_steps)
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evaluate_policy(model, env, n_eval_episodes=20, reward_threshold=90, warn=False)
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