mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-18 21:30:19 +00:00
5143cd19f7
* fix Atari in CI * fix dtype and atari extra * Update setup.py * remove 3.6 * note about how to install Atari * pendulum-v1 * atari v5 * black * fix pendulum capitalization * add minimum version * moved things in changelog to breaking changes * partial v5 fix * env update to pass tests * mismatch env version fixed * Fix tests after merge * Include autorom in setup.py * Blacken code * Fix dtype issue in more robust way * Fix GitLab CI: switch to Docker container with new black version * Remove workaround from GitLab. (May need to rebuild Docker for this though.) * Revert to v4 * Update setup.py * Apply suggestions from code review * Remove unnecessary autorom * Consistent gym versions Co-authored-by: J K Terry <justinkterry@gmail.com> Co-authored-by: Anssi <kaneran21@hotmail.com> Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org> Co-authored-by: modanesh <mohamad4danesh@gmail.com> Co-authored-by: Adam Gleave <adam@gleave.me>
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
import gym
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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.env_util import make_vec_env
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from stable_baselines3.common.evaluation import evaluate_policy
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class DummyMultiDiscreteSpace(gym.Env):
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def __init__(self, nvec):
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super(DummyMultiDiscreteSpace, self).__init__()
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self.observation_space = gym.spaces.MultiDiscrete(nvec)
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self.action_space = gym.spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
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def reset(self):
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return self.observation_space.sample()
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def step(self, action):
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return self.observation_space.sample(), 0.0, False, {}
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class DummyMultiBinary(gym.Env):
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def __init__(self, n):
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super(DummyMultiBinary, self).__init__()
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self.observation_space = gym.spaces.MultiBinary(n)
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self.action_space = gym.spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
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def reset(self):
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return self.observation_space.sample()
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def step(self, action):
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return self.observation_space.sample(), 0.0, False, {}
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@pytest.mark.parametrize("model_class", [SAC, TD3, DQN])
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@pytest.mark.parametrize("env", [DummyMultiDiscreteSpace([4, 3]), DummyMultiBinary(8)])
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def test_identity_spaces(model_class, env):
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"""
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Additional tests for DQ/SAC/TD3 to check observation space support
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for MultiDiscrete and MultiBinary.
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"""
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# DQN only support discrete actions
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if model_class == DQN:
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env.action_space = gym.spaces.Discrete(4)
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env = gym.wrappers.TimeLimit(env, max_episode_steps=100)
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model = model_class("MlpPolicy", env, gamma=0.5, seed=1, policy_kwargs=dict(net_arch=[64]))
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model.learn(total_timesteps=500)
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evaluate_policy(model, env, n_eval_episodes=5, warn=False)
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@pytest.mark.parametrize("model_class", [A2C, DDPG, DQN, PPO, SAC, TD3])
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@pytest.mark.parametrize("env", ["Pendulum-v1", "CartPole-v1"])
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def test_action_spaces(model_class, env):
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if model_class in [SAC, DDPG, TD3]:
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supported_action_space = env == "Pendulum-v1"
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elif model_class == DQN:
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supported_action_space = env == "CartPole-v1"
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elif model_class in [A2C, PPO]:
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supported_action_space = True
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if supported_action_space:
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model_class("MlpPolicy", env)
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else:
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with pytest.raises(AssertionError):
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model_class("MlpPolicy", env)
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@pytest.mark.parametrize("model_class", [A2C, PPO, DQN])
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@pytest.mark.parametrize("env", ["Taxi-v3"])
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def test_discrete_obs_space(model_class, env):
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env = make_vec_env(env, n_envs=2, seed=0)
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kwargs = {}
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if model_class == DQN:
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kwargs = dict(buffer_size=1000, learning_starts=100)
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else:
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kwargs = dict(n_steps=256)
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model_class("MlpPolicy", env, **kwargs).learn(256)
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