mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-06-05 00:00:04 +00:00
5ff176b2f1
* Add DDPG + TD3 with any number of critics * Allow any number of critics for SAC * Update doc * [ci skip] Update DDPG example * Remove unused parameter * Add DDPG to identity test * Fix computation with n_critics=1,3 * Update doc * Apply suggestions from code review Co-authored-by: Adam Gleave <adam@gleave.me> * Update docstrings for off-policy algos * Add check for sde Co-authored-by: Adam Gleave <adam@gleave.me>
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
import numpy as np
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import pytest
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from stable_baselines3 import A2C, PPO, SAC, TD3, DQN, DDPG
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from stable_baselines3.common.identity_env import (IdentityEnvBox, IdentityEnv,
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IdentityEnvMultiBinary, IdentityEnvMultiDiscrete)
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from stable_baselines3.common.vec_env import DummyVecEnv
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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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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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model = model_class('MlpPolicy', env_, gamma=0.5, seed=1, **kwargs).learn(n_steps)
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evaluate_policy(model, env_, n_eval_episodes=20, reward_threshold=90)
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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 = {
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A2C: 3500,
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PPO: 3000,
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SAC: 700,
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TD3: 500,
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DDPG: 500
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}[model_class]
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kwargs = dict(
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policy_kwargs=dict(net_arch=[64, 64]),
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seed=0,
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gamma=0.95
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)
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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)
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