mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-17 21:20:11 +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>
110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
import gym
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import numpy as np
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import pytest
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import torch as th
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from stable_baselines3 import A2C, DQN, PPO, SAC, TD3
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from stable_baselines3.common.envs import IdentityEnv
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from stable_baselines3.common.utils import get_device
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from stable_baselines3.common.vec_env import DummyVecEnv
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MODEL_LIST = [
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PPO,
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A2C,
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TD3,
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SAC,
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DQN,
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]
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class SubClassedBox(gym.spaces.Box):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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class CustomSubClassedSpaceEnv(gym.Env):
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def __init__(self):
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super().__init__()
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self.observation_space = SubClassedBox(-1, 1, shape=(2,), dtype=np.float32)
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self.action_space = SubClassedBox(-1, 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, np.random.rand() > 0.5, {}
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@pytest.mark.parametrize("model_class", MODEL_LIST)
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def test_auto_wrap(model_class):
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# test auto wrapping of env into a VecEnv
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# Use different environment for DQN
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if model_class is DQN:
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env_name = "CartPole-v0"
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else:
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env_name = "Pendulum-v1"
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env = gym.make(env_name)
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eval_env = gym.make(env_name)
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model = model_class("MlpPolicy", env)
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model.learn(100, eval_env=eval_env)
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@pytest.mark.parametrize("model_class", MODEL_LIST)
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@pytest.mark.parametrize("env_id", ["Pendulum-v1", "CartPole-v1"])
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@pytest.mark.parametrize("device", ["cpu", "cuda", "auto"])
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def test_predict(model_class, env_id, device):
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if device == "cuda" and not th.cuda.is_available():
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pytest.skip("CUDA not available")
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if env_id == "CartPole-v1":
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if model_class in [SAC, TD3]:
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return
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elif model_class in [DQN]:
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return
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# Test detection of different shapes by the predict method
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model = model_class("MlpPolicy", env_id, device=device)
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# Check that the policy is on the right device
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assert get_device(device).type == model.policy.device.type
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env = gym.make(env_id)
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vec_env = DummyVecEnv([lambda: gym.make(env_id), lambda: gym.make(env_id)])
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obs = env.reset()
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action, _ = model.predict(obs)
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assert action.shape == env.action_space.shape
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assert env.action_space.contains(action)
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vec_env_obs = vec_env.reset()
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action, _ = model.predict(vec_env_obs)
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assert action.shape[0] == vec_env_obs.shape[0]
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# Special case for DQN to check the epsilon greedy exploration
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if model_class == DQN:
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model.exploration_rate = 1.0
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action, _ = model.predict(obs, deterministic=False)
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assert action.shape == env.action_space.shape
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assert env.action_space.contains(action)
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action, _ = model.predict(vec_env_obs, deterministic=False)
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assert action.shape[0] == vec_env_obs.shape[0]
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def test_dqn_epsilon_greedy():
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env = IdentityEnv(2)
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model = DQN("MlpPolicy", env)
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model.exploration_rate = 1.0
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obs = env.reset()
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# is vectorized should not crash with discrete obs
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action, _ = model.predict(obs, deterministic=False)
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assert env.action_space.contains(action)
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@pytest.mark.parametrize("model_class", [A2C, SAC, PPO, TD3])
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def test_subclassed_space_env(model_class):
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env = CustomSubClassedSpaceEnv()
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model = model_class("MlpPolicy", env, policy_kwargs=dict(net_arch=[32]))
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model.learn(300)
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obs = env.reset()
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env.step(model.predict(obs))
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