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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 | import numpy as np
import pytest
from stable_baselines3 import A2C, DDPG, DQN, PPO, SAC, TD3
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.identity_env import IdentityEnv, IdentityEnvBox, IdentityEnvMultiBinary, IdentityEnvMultiDiscrete
from stable_baselines3.common.noise import NormalActionNoise
from stable_baselines3.common.vec_env import DummyVecEnv
DIM = 4
@pytest.mark.parametrize("model_class", [A2C, PPO, DQN])
@pytest.mark.parametrize("env", [IdentityEnv(DIM), IdentityEnvMultiDiscrete(DIM), IdentityEnvMultiBinary(DIM)])
def test_discrete(model_class, env):
env_ = DummyVecEnv([lambda: env])
kwargs = {}
n_steps = 3000
if model_class == DQN:
kwargs = dict(learning_starts=0)
n_steps = 4000
# DQN only support discrete actions
if isinstance(env, (IdentityEnvMultiDiscrete, IdentityEnvMultiBinary)):
return
model = model_class("MlpPolicy", env_, gamma=0.4, seed=1, **kwargs).learn(n_steps)
evaluate_policy(model, env_, n_eval_episodes=20, reward_threshold=90, warn=False)
obs = env.reset()
assert np.shape(model.predict(obs)[0]) == np.shape(obs)
@pytest.mark.parametrize("model_class", [A2C, PPO, SAC, DDPG, TD3])
def test_continuous(model_class):
env = IdentityEnvBox(eps=0.5)
n_steps = {A2C: 3500, PPO: 3000, SAC: 700, TD3: 500, DDPG: 500}[model_class]
kwargs = dict(policy_kwargs=dict(net_arch=[64, 64]), seed=0, gamma=0.95)
if model_class in [TD3]:
n_actions = 1
action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions))
kwargs["action_noise"] = action_noise
model = model_class("MlpPolicy", env, **kwargs).learn(n_steps)
evaluate_policy(model, env, n_eval_episodes=20, reward_threshold=90, warn=False)
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