stable-baselines3/tests/test_run.py
2019-10-07 16:26:03 +02:00

41 lines
1.5 KiB
Python

import os
import numpy as np
from torchy_baselines import TD3, CEMRL, PPO, SAC
from torchy_baselines.common.noise import NormalActionNoise
action_noise = NormalActionNoise(np.zeros(1), 0.1 * np.ones(1))
def test_td3():
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)
model.save("test_save")
model.load("test_save")
os.remove("test_save.pth")
def test_cemrl():
model = CEMRL('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[16]), pop_size=2, n_grad=1,
learning_starts=100, verbose=1, create_eval_env=True, action_noise=action_noise)
model.learn(total_timesteps=1000, eval_freq=500)
model.save("test_save")
model.load("test_save")
os.remove("test_save.pth")
def test_ppo():
model = PPO('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[16]), verbose=1, create_eval_env=True)
model.learn(total_timesteps=1000, eval_freq=500)
# model.save("test_save")
# model.load("test_save")
# os.remove("test_save.pth")
def test_sac():
model = SAC('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[64, 64]),
learning_starts=100, verbose=1, create_eval_env=True, ent_coef='auto',
action_noise=NormalActionNoise(np.zeros(1), np.zeros(1)))
model.learn(total_timesteps=1000, eval_freq=500)