def test_frame_stack():
base_obs_space = {
"a_{}".format(idx): Box(low=np.float32(0.),
high=np.float32(10.),
shape=[2, 3])
for idx in range(2)
}
base_obs = {
"a_{}".format(idx): np.zeros([2, 3]) + np.arange(3) + idx
for idx in range(2)
}
base_env = DummyEnv(base_obs, base_obs_space, base_act_spaces)
env = frame_stack(base_env, 4)
obs = env.reset()
assert obs.shape == (2, 3, 4)
first_obs = env.step(2)
assert np.all(np.equal(first_obs[:, :, -1], base_obs["a_1"]))
assert np.all(np.equal(first_obs[:, :, :-1], 0))
base_obs = {"a_{}".format(idx): idx + 3 for idx in range(2)}
base_env = DummyEnv(base_obs, base_act_spaces, base_act_spaces)
env = frame_stack(base_env, 4)
obs = env.reset()
assert env.observation_spaces[env.agent_selection].n == 5**4
first_obs = env.step(2)
assert first_obs == 4
second_obs = env.step(2)
assert second_obs == 3 + 3 * 5
for x in range(100):
nth_obs = env.step(2)
assert nth_obs == ((3 * 5 + 3) * 5 + 3) * 5 + 3
def env_creator(args):
env = game_env.env(obs_type='grayscale_image')
#env = clip_reward(env, lower_bound=-1, upper_bound=1)
#env = sticky_actions(env, repeat_action_probability=0.25)
env = resize(env, 84, 84)
#env = color_reduction(env, mode='full')
env = frame_stack(env, 4)
env = agent_indicator(env, type_only=False)
#env = frame_skip(env, 4)
return env
def test_basic_wrappers():
wrappers = [
aec_wrappers.color_reduction(new_dummy(), "R"),
aec_wrappers.down_scale(new_dummy(), x_scale=5, y_scale=10),
aec_wrappers.dtype(new_dummy(), np.int32),
aec_wrappers.flatten(new_dummy()),
aec_wrappers.reshape(new_dummy(), (64, 3)),
aec_wrappers.normalize_obs(new_dummy(), env_min=-1, env_max=5.),
aec_wrappers.frame_stack(new_dummy(), 8),
aec_wrappers.pad_observations(new_dummy()),
]
for env in wrappers:
obs = env.reset()
first_obs = env.observe("a_1")
shape=[8, 8, 3])
for idx in range(2)
}
base_act_spaces = {"a_{}".format(idx): Discrete(5) for idx in range(2)}
return DummyEnv(base_obs, base_obs_space, base_act_spaces)
wrappers = [
aec_wrappers.color_reduction(new_dummy(), "R"),
aec_wrappers.down_scale(new_dummy(), x_scale=5, y_scale=10),
aec_wrappers.dtype(new_dummy(), np.int32),
aec_wrappers.flatten(new_dummy()),
aec_wrappers.reshape(new_dummy(), (64, 3)),
aec_wrappers.normalize_obs(new_dummy(), env_min=-1, env_max=5.),
aec_wrappers.frame_stack(new_dummy(), 8),
aec_wrappers.pad_observations(new_dummy()),
aec_wrappers.pad_action_space(new_dummy()),
aec_wrappers.continuous_actions(new_dummy()),
aec_wrappers.agent_indicator(new_dummy(), True),
aec_wrappers.agent_indicator(new_dummy(), False),
]
@pytest.mark.parametrize("env", wrappers)
def test_basic_wrappers(env):
obs = env.reset()
act_space = env.action_spaces[env.agent_selection]
obs_space = env.observation_spaces[env.agent_selection]
first_obs = env.observe("a_0")
print(obs_space.low.shape)