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import shutil
import gym
import numpy as np
import pytest
import torch as th
from stable_baselines3 import A2C
from stable_baselines3.common.atari_wrappers import ClipRewardEnv
from stable_baselines3.common.env_util import is_wrapped, make_atari_env, make_vec_env, unwrap_wrapper
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.noise import ActionNoise, OrnsteinUhlenbeckActionNoise, VectorizedActionNoise
from stable_baselines3.common.utils import polyak_update, zip_strict
from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv
@pytest.mark.parametrize("env_id", ["CartPole-v1", lambda: gym.make("CartPole-v1")])
@pytest.mark.parametrize("n_envs", [1, 2])
@pytest.mark.parametrize("vec_env_cls", [None, SubprocVecEnv])
@pytest.mark.parametrize("wrapper_class", [None, gym.wrappers.TimeLimit])
def test_make_vec_env(env_id, n_envs, vec_env_cls, wrapper_class):
env = make_vec_env(env_id, n_envs, vec_env_cls=vec_env_cls, wrapper_class=wrapper_class, monitor_dir=None, seed=0)
assert env.num_envs == n_envs
if vec_env_cls is None:
assert isinstance(env, DummyVecEnv)
if wrapper_class is not None:
assert isinstance(env.envs[0], wrapper_class)
else:
assert isinstance(env.envs[0], Monitor)
else:
assert isinstance(env, SubprocVecEnv)
# Kill subprocesses
env.close()
@pytest.mark.parametrize("env_id", ["BreakoutNoFrameskip-v4"])
@pytest.mark.parametrize("n_envs", [1, 2])
@pytest.mark.parametrize("wrapper_kwargs", [None, dict(clip_reward=False, screen_size=60)])
def test_make_atari_env(env_id, n_envs, wrapper_kwargs):
env_id = "BreakoutNoFrameskip-v4"
env = make_atari_env(env_id, n_envs, wrapper_kwargs=wrapper_kwargs, monitor_dir=None, seed=0)
assert env.num_envs == n_envs
obs = env.reset()
new_obs, reward, _, _ = env.step([env.action_space.sample() for _ in range(n_envs)])
assert obs.shape == new_obs.shape
# Wrapped into DummyVecEnv
wrapped_atari_env = env.envs[0]
if wrapper_kwargs is not None:
assert obs.shape == (n_envs, 60, 60, 1)
assert wrapped_atari_env.observation_space.shape == (60, 60, 1)
assert not isinstance(wrapped_atari_env.env, ClipRewardEnv)
else:
assert obs.shape == (n_envs, 84, 84, 1)
assert wrapped_atari_env.observation_space.shape == (84, 84, 1)
assert isinstance(wrapped_atari_env.env, ClipRewardEnv)
assert np.max(np.abs(reward)) < 1.0
def test_vec_env_kwargs():
env = make_vec_env("MountainCarContinuous-v0", n_envs=1, seed=0, env_kwargs={"goal_velocity": 0.11})
assert env.get_attr("goal_velocity")[0] == 0.11
def test_vec_env_monitor_kwargs():
env = make_vec_env("MountainCarContinuous-v0", n_envs=1, seed=0, monitor_kwargs={"allow_early_resets": False})
assert env.get_attr("allow_early_resets")[0] is False
env = make_atari_env("BreakoutNoFrameskip-v4", n_envs=1, seed=0, monitor_kwargs={"allow_early_resets": False})
assert env.get_attr("allow_early_resets")[0] is False
env = make_vec_env("MountainCarContinuous-v0", n_envs=1, seed=0, monitor_kwargs={"allow_early_resets": True})
assert env.get_attr("allow_early_resets")[0] is True
env = make_atari_env("BreakoutNoFrameskip-v4", n_envs=1, seed=0, monitor_kwargs={"allow_early_resets": True})
assert env.get_attr("allow_early_resets")[0] is True
def test_env_auto_monitor_wrap():
env = gym.make("Pendulum-v0")
model = A2C("MlpPolicy", env)
assert model.env.env_is_wrapped(Monitor)[0] is True
env = Monitor(env)
model = A2C("MlpPolicy", env)
assert model.env.env_is_wrapped(Monitor)[0] is True
model = A2C("MlpPolicy", "Pendulum-v0")
assert model.env.env_is_wrapped(Monitor)[0] is True
def test_custom_vec_env(tmp_path):
"""
Stand alone test for a special case (passing a custom VecEnv class) to avoid doubling the number of tests.
"""
monitor_dir = tmp_path / "test_make_vec_env/"
env = make_vec_env(
"CartPole-v1",
n_envs=1,
monitor_dir=monitor_dir,
seed=0,
vec_env_cls=SubprocVecEnv,
vec_env_kwargs={"start_method": None},
)
assert env.num_envs == 1
assert isinstance(env, SubprocVecEnv)
assert os.path.isdir(monitor_dir)
# Kill subprocess
env.close()
# Cleanup folder
shutil.rmtree(monitor_dir)
# This should fail because DummyVecEnv does not have any keyword argument
with pytest.raises(TypeError):
make_vec_env("CartPole-v1", n_envs=1, vec_env_kwargs={"dummy": False})
def test_evaluate_policy():
model = A2C("MlpPolicy", "Pendulum-v0", seed=0)
n_steps_per_episode, n_eval_episodes = 200, 2
model.n_callback_calls = 0
def dummy_callback(locals_, _globals):
locals_["model"].n_callback_calls += 1
_, episode_lengths = evaluate_policy(
model,
model.get_env(),
n_eval_episodes,
deterministic=True,
render=False,
callback=dummy_callback,
reward_threshold=None,
return_episode_rewards=True,
)
n_steps = sum(episode_lengths)
assert n_steps == n_steps_per_episode * n_eval_episodes
assert n_steps == model.n_callback_calls
# Reaching a mean reward of zero is impossible with the Pendulum env
with pytest.raises(AssertionError):
evaluate_policy(model, model.get_env(), n_eval_episodes, reward_threshold=0.0)
episode_rewards, _ = evaluate_policy(model, model.get_env(), n_eval_episodes, return_episode_rewards=True)
assert len(episode_rewards) == n_eval_episodes
# Test that warning is given about no monitor
eval_env = gym.make("Pendulum-v0")
with pytest.warns(UserWarning):
_ = evaluate_policy(model, eval_env, n_eval_episodes)
class ZeroRewardWrapper(gym.RewardWrapper):
def reward(self, reward):
return reward * 0
class AlwaysDoneWrapper(gym.Wrapper):
# Pretends that environment only has single step for each
# episode.
def __init__(self, env):
super(AlwaysDoneWrapper, self).__init__(env)
self.last_obs = None
self.needs_reset = True
def step(self, action):
obs, reward, done, info = self.env.step(action)
self.needs_reset = done
self.last_obs = obs
return obs, reward, True, info
def reset(self, **kwargs):
if self.needs_reset:
obs = self.env.reset(**kwargs)
self.last_obs = obs
self.needs_reset = False
return self.last_obs
@pytest.mark.parametrize("vec_env_class", [None, DummyVecEnv, SubprocVecEnv])
def test_evaluate_policy_monitors(vec_env_class):
# Test that results are correct with monitor environments.
# Also test VecEnvs
n_eval_episodes = 2
env_id = "CartPole-v0"
model = A2C("MlpPolicy", env_id, seed=0)
def make_eval_env(with_monitor, wrapper_class=gym.Wrapper):
# Make eval environment with or without monitor in root,
# and additionally wrapped with another wrapper (after Monitor).
env = None
if vec_env_class is None:
# No vecenv, traditional env
env = gym.make(env_id)
if with_monitor:
env = Monitor(env)
env = wrapper_class(env)
else:
if with_monitor:
env = vec_env_class([lambda: wrapper_class(Monitor(gym.make(env_id)))])
else:
env = vec_env_class([lambda: wrapper_class(gym.make(env_id))])
return env
# Test that evaluation with VecEnvs works as expected
eval_env = make_eval_env(with_monitor=True)
_ = evaluate_policy(model, eval_env, n_eval_episodes)
eval_env.close()
# Warning without Monitor
eval_env = make_eval_env(with_monitor=False)
with pytest.warns(UserWarning):
_ = evaluate_policy(model, eval_env, n_eval_episodes)
eval_env.close()
# Test that we gather correct reward with Monitor wrapper
# Sanity check that we get zero-reward without Monitor
eval_env = make_eval_env(with_monitor=False, wrapper_class=ZeroRewardWrapper)
average_reward, _ = evaluate_policy(model, eval_env, n_eval_episodes, warn=False)
assert average_reward == 0.0, "ZeroRewardWrapper wrapper for testing did not work"
eval_env.close()
# Should get non-zero-rewards with Monitor (true reward)
eval_env = make_eval_env(with_monitor=True, wrapper_class=ZeroRewardWrapper)
average_reward, _ = evaluate_policy(model, eval_env, n_eval_episodes)
assert average_reward > 0.0, "evaluate_policy did not get reward from Monitor"
eval_env.close()
# Test that we also track correct episode dones, not the wrapped ones.
# Sanity check that we get only one step per episode.
eval_env = make_eval_env(with_monitor=False, wrapper_class=AlwaysDoneWrapper)
episode_rewards, episode_lengths = evaluate_policy(
model, eval_env, n_eval_episodes, return_episode_rewards=True, warn=False
)
assert all(map(lambda l: l == 1, episode_lengths)), "AlwaysDoneWrapper did not fix episode lengths to one"
eval_env.close()
# Should get longer episodes with with Monitor (true episodes)
eval_env = make_eval_env(with_monitor=True, wrapper_class=AlwaysDoneWrapper)
episode_rewards, episode_lengths = evaluate_policy(model, eval_env, n_eval_episodes, return_episode_rewards=True)
assert all(map(lambda l: l > 1, episode_lengths)), "evaluate_policy did not get episode lengths from Monitor"
eval_env.close()
def test_vec_noise():
num_envs = 4
num_actions = 10
mu = np.zeros(num_actions)
sigma = np.ones(num_actions) * 0.4
base: ActionNoise = OrnsteinUhlenbeckActionNoise(mu, sigma)
with pytest.raises(ValueError):
vec = VectorizedActionNoise(base, -1)
with pytest.raises(ValueError):
vec = VectorizedActionNoise(base, None)
with pytest.raises(ValueError):
vec = VectorizedActionNoise(base, "whatever")
vec = VectorizedActionNoise(base, num_envs)
assert vec.n_envs == num_envs
assert vec().shape == (num_envs, num_actions)
assert not (vec() == base()).all()
with pytest.raises(ValueError):
vec = VectorizedActionNoise(None, num_envs)
with pytest.raises(TypeError):
vec = VectorizedActionNoise(12, num_envs)
with pytest.raises(AssertionError):
vec.noises = []
with pytest.raises(TypeError):
vec.noises = None
with pytest.raises(ValueError):
vec.noises = [None] * vec.n_envs
with pytest.raises(AssertionError):
vec.noises = [base] * (num_envs - 1)
assert all(isinstance(noise, type(base)) for noise in vec.noises)
assert len(vec.noises) == num_envs
def test_polyak():
param1, param2 = th.nn.Parameter(th.ones((5, 5))), th.nn.Parameter(th.zeros((5, 5)))
target1, target2 = th.nn.Parameter(th.ones((5, 5))), th.nn.Parameter(th.zeros((5, 5)))
tau = 0.1
polyak_update([param1], [param2], tau)
with th.no_grad():
for param, target_param in zip([target1], [target2]):
target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
assert th.allclose(param1, target1)
assert th.allclose(param2, target2)
def test_zip_strict():
# Iterables with different lengths
list_a = [0, 1]
list_b = [1, 2, 3]
# zip does not raise any error
for _, _ in zip(list_a, list_b):
pass
# zip_strict does raise an error
with pytest.raises(ValueError):
for _, _ in zip_strict(list_a, list_b):
pass
# same length, should not raise an error
for _, _ in zip_strict(list_a, list_b[: len(list_a)]):
pass
def test_cmd_util_rename():
"""Test that importing cmd_util still works but raises warning"""
with pytest.warns(FutureWarning):
from stable_baselines3.common.cmd_util import make_vec_env # noqa: F401
def test_is_wrapped():
"""Test that is_wrapped correctly detects wraps"""
env = gym.make("Pendulum-v0")
env = gym.Wrapper(env)
assert not is_wrapped(env, Monitor)
monitor_env = Monitor(env)
assert is_wrapped(monitor_env, Monitor)
env = gym.Wrapper(monitor_env)
assert is_wrapped(env, Monitor)
# Test that unwrap works as expected
assert unwrap_wrapper(env, Monitor) == monitor_env
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