File size: 4,626 Bytes
a89d35f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | import gym
import numpy as np
import pytest
from gym import spaces
from stable_baselines3.common.bit_flipping_env import BitFlippingEnv
from stable_baselines3.common.env_checker import check_env
from stable_baselines3.common.identity_env import (
FakeImageEnv,
IdentityEnv,
IdentityEnvBox,
IdentityEnvMultiBinary,
IdentityEnvMultiDiscrete,
)
ENV_CLASSES = [BitFlippingEnv, IdentityEnv, IdentityEnvBox, IdentityEnvMultiBinary, IdentityEnvMultiDiscrete, FakeImageEnv]
@pytest.mark.parametrize("env_id", ["CartPole-v0", "Pendulum-v0"])
def test_env(env_id):
"""
Check that environmnent integrated in Gym pass the test.
:param env_id: (str)
"""
env = gym.make(env_id)
with pytest.warns(None) as record:
check_env(env)
# Pendulum-v0 will produce a warning because the action space is
# in [-2, 2] and not [-1, 1]
if env_id == "Pendulum-v0":
assert len(record) == 1
else:
# The other environments must pass without warning
assert len(record) == 0
@pytest.mark.parametrize("env_class", ENV_CLASSES)
def test_custom_envs(env_class):
env = env_class()
check_env(env)
def test_high_dimension_action_space():
"""
Test for continuous action space
with more than one action.
"""
env = FakeImageEnv()
# Patch the action space
env.action_space = spaces.Box(low=-1, high=1, shape=(20,), dtype=np.float32)
# Patch to avoid error
def patched_step(_action):
return env.observation_space.sample(), 0.0, False, {}
env.step = patched_step
check_env(env)
@pytest.mark.parametrize(
"new_obs_space",
[
# Small image
spaces.Box(low=0, high=255, shape=(32, 32, 3), dtype=np.uint8),
# Range not in [0, 255]
spaces.Box(low=0, high=1, shape=(64, 64, 3), dtype=np.uint8),
# Wrong dtype
spaces.Box(low=0, high=255, shape=(64, 64, 3), dtype=np.float32),
# Not an image, it should be a 1D vector
spaces.Box(low=-1, high=1, shape=(64, 3), dtype=np.float32),
# Tuple space is not supported by SB
spaces.Tuple([spaces.Discrete(5), spaces.Discrete(10)]),
# Dict space is not supported by SB when env is not a GoalEnv
spaces.Dict({"position": spaces.Discrete(5)}),
],
)
def test_non_default_spaces(new_obs_space):
env = FakeImageEnv()
env.observation_space = new_obs_space
# Patch methods to avoid errors
env.reset = new_obs_space.sample
def patched_step(_action):
return new_obs_space.sample(), 0.0, False, {}
env.step = patched_step
with pytest.warns(UserWarning):
check_env(env)
def check_reset_assert_error(env, new_reset_return):
"""
Helper to check that the error is caught.
:param env: (gym.Env)
:param new_reset_return: (Any)
"""
def wrong_reset():
return new_reset_return
# Patch the reset method with a wrong one
env.reset = wrong_reset
with pytest.raises(AssertionError):
check_env(env)
def test_common_failures_reset():
"""
Test that common failure cases of the `reset_method` are caught
"""
env = IdentityEnvBox()
# Return an observation that does not match the observation_space
check_reset_assert_error(env, np.ones((3,)))
# The observation is not a numpy array
check_reset_assert_error(env, 1)
# Return not only the observation
check_reset_assert_error(env, (env.observation_space.sample(), False))
def check_step_assert_error(env, new_step_return=()):
"""
Helper to check that the error is caught.
:param env: (gym.Env)
:param new_step_return: (tuple)
"""
def wrong_step(_action):
return new_step_return
# Patch the step method with a wrong one
env.step = wrong_step
with pytest.raises(AssertionError):
check_env(env)
def test_common_failures_step():
"""
Test that common failure cases of the `step` method are caught
"""
env = IdentityEnvBox()
# Wrong shape for the observation
check_step_assert_error(env, (np.ones((4,)), 1.0, False, {}))
# Obs is not a numpy array
check_step_assert_error(env, (1, 1.0, False, {}))
# Return a wrong reward
check_step_assert_error(env, (env.observation_space.sample(), np.ones(1), False, {}))
# Info dict is not returned
check_step_assert_error(env, (env.observation_space.sample(), 0.0, False))
# Done is not a boolean
check_step_assert_error(env, (env.observation_space.sample(), 0.0, 3.0, {}))
check_step_assert_error(env, (env.observation_space.sample(), 0.0, 1, {}))
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