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* Add auto formatting with black and isort * Reformat code * Ignore typing errors * Add note about line length * Add minimum version for isort * Add commit-checks * Update docker image * Fixed lost import (during last merge) * Fix opencv dependency
182 lines
6 KiB
Python
182 lines
6 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 SAC, TD3
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from stable_baselines3.common.running_mean_std import RunningMeanStd
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from stable_baselines3.common.vec_env import (
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DummyVecEnv,
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VecFrameStack,
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VecNormalize,
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sync_envs_normalization,
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unwrap_vec_normalize,
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)
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ENV_ID = "Pendulum-v0"
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def make_env():
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return gym.make(ENV_ID)
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def check_rms_equal(rmsa, rmsb):
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assert np.all(rmsa.mean == rmsb.mean)
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assert np.all(rmsa.var == rmsb.var)
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assert np.all(rmsa.count == rmsb.count)
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def check_vec_norm_equal(norma, normb):
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assert norma.observation_space == normb.observation_space
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assert norma.action_space == normb.action_space
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assert norma.num_envs == normb.num_envs
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check_rms_equal(norma.obs_rms, normb.obs_rms)
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check_rms_equal(norma.ret_rms, normb.ret_rms)
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assert norma.clip_obs == normb.clip_obs
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assert norma.clip_reward == normb.clip_reward
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assert norma.norm_obs == normb.norm_obs
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assert norma.norm_reward == normb.norm_reward
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assert np.all(norma.ret == normb.ret)
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assert norma.gamma == normb.gamma
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assert norma.epsilon == normb.epsilon
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assert norma.training == normb.training
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def _make_warmstart_cartpole():
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"""Warm-start VecNormalize by stepping through CartPole"""
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venv = DummyVecEnv([lambda: gym.make("CartPole-v1")])
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venv = VecNormalize(venv)
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venv.reset()
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venv.get_original_obs()
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for _ in range(100):
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actions = [venv.action_space.sample()]
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venv.step(actions)
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return venv
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def test_runningmeanstd():
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"""Test RunningMeanStd object"""
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for (x_1, x_2, x_3) in [
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(np.random.randn(3), np.random.randn(4), np.random.randn(5)),
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(np.random.randn(3, 2), np.random.randn(4, 2), np.random.randn(5, 2)),
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]:
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rms = RunningMeanStd(epsilon=0.0, shape=x_1.shape[1:])
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x_cat = np.concatenate([x_1, x_2, x_3], axis=0)
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moments_1 = [x_cat.mean(axis=0), x_cat.var(axis=0)]
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rms.update(x_1)
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rms.update(x_2)
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rms.update(x_3)
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moments_2 = [rms.mean, rms.var]
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assert np.allclose(moments_1, moments_2)
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def test_vec_env(tmp_path):
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"""Test VecNormalize Object"""
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clip_obs = 0.5
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clip_reward = 5.0
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orig_venv = DummyVecEnv([make_env])
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norm_venv = VecNormalize(orig_venv, norm_obs=True, norm_reward=True, clip_obs=clip_obs, clip_reward=clip_reward)
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_, done = norm_venv.reset(), [False]
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while not done[0]:
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actions = [norm_venv.action_space.sample()]
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obs, rew, done, _ = norm_venv.step(actions)
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assert np.max(np.abs(obs)) <= clip_obs
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assert np.max(np.abs(rew)) <= clip_reward
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path = tmp_path / "vec_normalize"
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norm_venv.save(path)
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deserialized = VecNormalize.load(path, venv=orig_venv)
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check_vec_norm_equal(norm_venv, deserialized)
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def test_get_original():
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venv = _make_warmstart_cartpole()
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for _ in range(3):
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actions = [venv.action_space.sample()]
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obs, rewards, _, _ = venv.step(actions)
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obs = obs[0]
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orig_obs = venv.get_original_obs()[0]
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rewards = rewards[0]
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orig_rewards = venv.get_original_reward()[0]
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assert np.all(orig_rewards == 1)
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assert orig_obs.shape == obs.shape
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assert orig_rewards.dtype == rewards.dtype
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assert not np.array_equal(orig_obs, obs)
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assert not np.array_equal(orig_rewards, rewards)
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np.testing.assert_allclose(venv.normalize_obs(orig_obs), obs)
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np.testing.assert_allclose(venv.normalize_reward(orig_rewards), rewards)
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def test_normalize_external():
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venv = _make_warmstart_cartpole()
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rewards = np.array([1, 1])
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norm_rewards = venv.normalize_reward(rewards)
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assert norm_rewards.shape == rewards.shape
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# Episode return is almost always >= 1 in CartPole. So reward should shrink.
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assert np.all(norm_rewards < 1)
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@pytest.mark.parametrize("model_class", [SAC, TD3])
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def test_offpolicy_normalization(model_class):
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env = DummyVecEnv([make_env])
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env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10.0, clip_reward=10.0)
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eval_env = DummyVecEnv([make_env])
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eval_env = VecNormalize(eval_env, training=False, norm_obs=True, norm_reward=False, clip_obs=10.0, clip_reward=10.0)
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model = model_class("MlpPolicy", env, verbose=1, policy_kwargs=dict(net_arch=[64]))
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model.learn(total_timesteps=1000, eval_env=eval_env, eval_freq=500)
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# Check getter
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assert isinstance(model.get_vec_normalize_env(), VecNormalize)
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def test_sync_vec_normalize():
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env = DummyVecEnv([make_env])
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assert unwrap_vec_normalize(env) is None
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env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=100.0, clip_reward=100.0)
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assert isinstance(unwrap_vec_normalize(env), VecNormalize)
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env = VecFrameStack(env, 1)
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assert isinstance(unwrap_vec_normalize(env), VecNormalize)
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eval_env = DummyVecEnv([make_env])
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eval_env = VecNormalize(eval_env, training=False, norm_obs=True, norm_reward=True, clip_obs=100.0, clip_reward=100.0)
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eval_env = VecFrameStack(eval_env, 1)
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env.seed(0)
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env.action_space.seed(0)
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env.reset()
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# Initialize running mean
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latest_reward = None
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for _ in range(100):
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_, latest_reward, _, _ = env.step([env.action_space.sample()])
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# Check that unnormalized reward is same as original reward
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original_latest_reward = env.get_original_reward()
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assert np.allclose(original_latest_reward, env.unnormalize_reward(latest_reward))
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obs = env.reset()
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dummy_rewards = np.random.rand(10)
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original_obs = env.get_original_obs()
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# Check that unnormalization works
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assert np.allclose(original_obs, env.unnormalize_obs(obs))
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# Normalization must be different (between different environments)
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assert not np.allclose(obs, eval_env.normalize_obs(original_obs))
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# Test syncing of parameters
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sync_envs_normalization(env, eval_env)
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# Now they must be synced
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assert np.allclose(obs, eval_env.normalize_obs(original_obs))
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assert np.allclose(env.normalize_reward(dummy_rewards), eval_env.normalize_reward(dummy_rewards))
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