mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-18 21:30:19 +00:00
507ed1762e
* Add multi-env training support for SAC * Fix for dict obs * Pytype fixes * Fix assert on number of envs * Remove for loop * Add support for Dict obs * Start cleanup * Update doc and bug fix * Add support for vectorized action noise and add multi env example for off-policy * Update version * Bug fix with VecNormalize * Update README table * Update variable names * Update changelog and version * Update doc and fix for `gradient_steps=-1` * Add test for `gradient_steps=-1` * Disable pytype pyi errors * Fix for DQN * Update comment on deepcopy * Remove episode_reward field * Fix RolloutReturn * Avoid modification by reference * Fix error message Co-authored-by: Anssi <kaneran21@hotmail.com>
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-v0", "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-v0"
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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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