mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-31 23:28:05 +00:00
91adefdb4b
* multicategorical dist and test * fixed List annotation * bernoulli dist and test * added distributions to preprocessing (needs testing) * fixed and tested distributions * added changelog and fixed ppo policy * minor fix * dist fixes, added test_spaces * clean up * modified changelog * additional fixes * minor changelog mod * hot encoding fix, flake8 clean up * lint tests * preprocessing fix * fixed bernoulli bug * removed commented prints * Update changelog.rst * included suggested modifications * linting fix * increased space dim * Update doc and tests Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
import numpy as np
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import pytest
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import gym
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from stable_baselines3 import SAC, TD3
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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])
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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 SAC/TD3 to check observation space support
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for MultiDiscrete and MultiBinary.
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"""
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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)
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