stable-baselines3/tests/test_run.py

import numpy as np
import pytest

from stable_baselines3 import A2C, PPO, SAC, TD3, DQN
from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise

normal_action_noise = NormalActionNoise(np.zeros(1), 0.1 * np.ones(1))


@pytest.mark.parametrize('action_noise', [normal_action_noise, OrnsteinUhlenbeckActionNoise(np.zeros(1), 0.1 * np.ones(1))])
def test_td3(action_noise):
    model = TD3('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[64, 64]),
                learning_starts=100, verbose=1, create_eval_env=True, action_noise=action_noise)
    model.learn(total_timesteps=1000, eval_freq=500)


@pytest.mark.parametrize("env_id", ['CartPole-v1', 'Pendulum-v0'])
def test_a2c(env_id):
    model = A2C('MlpPolicy', env_id, seed=0, policy_kwargs=dict(net_arch=[16]), verbose=1, create_eval_env=True)
    model.learn(total_timesteps=1000, eval_freq=500)


@pytest.mark.parametrize("env_id", ['CartPole-v1', 'Pendulum-v0'])
@pytest.mark.parametrize("clip_range_vf", [None, 0.2, -0.2])
def test_ppo(env_id, clip_range_vf):
    if clip_range_vf is not None and clip_range_vf < 0:
        # Should throw an error
        with pytest.raises(AssertionError):
            model = PPO('MlpPolicy', env_id, seed=0, policy_kwargs=dict(net_arch=[16]), verbose=1, create_eval_env=True,
                        clip_range_vf=clip_range_vf)
    else:
        model = PPO('MlpPolicy', env_id, seed=0, policy_kwargs=dict(net_arch=[16]), verbose=1, create_eval_env=True,
                    clip_range_vf=clip_range_vf)
        model.learn(total_timesteps=1000, eval_freq=500)


@pytest.mark.parametrize("ent_coef", ['auto', 0.01, 'auto_0.01'])
def test_sac(ent_coef):
    model = SAC('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[64, 64]),
                learning_starts=100, verbose=1, create_eval_env=True, ent_coef=ent_coef,
                action_noise=NormalActionNoise(np.zeros(1), np.zeros(1)))
    model.learn(total_timesteps=1000, eval_freq=500)


def test_dqn():
    model = DQN('MlpPolicy', 'CartPole-v1', policy_kwargs=dict(net_arch=[64, 64]),
                learning_starts=500, buffer_size=500, learning_rate=3e-4, verbose=1, create_eval_env=True)
    model.learn(total_timesteps=1000, eval_freq=500)