class TestMaxAndSkip:
def setup_method(self):
self.env = DummyDiscretePixelEnv(random=False)
self.env_wrap = MaxAndSkip(DummyDiscretePixelEnv(random=False), skip=4)
def teardown_method(self):
self.env.close()
self.env_wrap.close()
def test_max_and_skip_reset(self):
np.testing.assert_array_equal(self.env.reset(), self.env_wrap.reset())
def test_max_and_skip_step(self):
self.env.reset()
self.env_wrap.reset()
obs_wrap, reward_wrap, _, _ = self.env_wrap.step(1)
reward = 0
for _ in range(4):
obs, r, _, _ = self.env.step(1)
reward += r
np.testing.assert_array_equal(obs, obs_wrap)
np.testing.assert_array_equal(reward, reward_wrap)
# done=True because both env stepped more than 4 times in total
obs_wrap, _, done_wrap, _ = self.env_wrap.step(1)
obs, _, done, _ = self.env.step(1)
assert done
assert done_wrap
np.testing.assert_array_equal(obs, obs_wrap)
class TestGrayscale:
def setup_method(self):
self.env = DummyDiscretePixelEnv(random=False)
self.env_g = Grayscale(DummyDiscretePixelEnv(random=False))
def teardown_method(self):
self.env.close()
self.env_g.close()
def test_grayscale_invalid_environment_type(self):
with pytest.raises(ValueError):
self.env.observation_space = gym.spaces.Discrete(64)
Grayscale(self.env)
def test_grayscale_invalid_environment_shape(self):
with pytest.raises(ValueError):
self.env.observation_space = gym.spaces.Box(low=0,
high=255,
shape=(4, ),
dtype=np.uint8)
Grayscale(self.env)
def test_grayscale_observation_space(self):
assert self.env_g.observation_space.shape == (
self.env.observation_space.shape[:-1])
def test_grayscale_reset(self):
"""
RGB to grayscale conversion using scikit-image.
Weights used for conversion:
Y = 0.2125 R + 0.7154 G + 0.0721 B
Reference:
http://scikit-image.org/docs/dev/api/skimage.color.html#skimage.color.rgb2grey
"""
grayscale_output = np.round(
np.dot(self.env.reset()[:, :, :3],
[0.2125, 0.7154, 0.0721])).astype(np.uint8)
np.testing.assert_array_almost_equal(grayscale_output,
self.env_g.reset())
def test_grayscale_step(self):
self.env.reset()
self.env_g.reset()
obs, _, _, _ = self.env.step(1)
obs_g, _, _, _ = self.env_g.step(1)
grayscale_output = np.round(
np.dot(obs[:, :, :3], [0.2125, 0.7154, 0.0721])).astype(np.uint8)
np.testing.assert_array_almost_equal(grayscale_output, obs_g)
def test_get_action(self, hidden_channels, kernel_sizes, strides,
hidden_sizes):
"""Test get_action function."""
env = DummyDiscretePixelEnv()
env = self._initialize_obs_env(env)
policy = CategoricalCNNPolicy(env=env,
kernel_sizes=kernel_sizes,
hidden_channels=hidden_channels,
strides=strides,
hidden_sizes=hidden_sizes)
env.reset()
obs, _, _, _ = env.step(1)
action, _ = policy.get_action(obs)
assert env.action_space.contains(action)
def test_get_actions(self, hidden_channels, kernel_sizes, strides,
hidden_sizes):
"""Test get_actions function with akro.Image observation space."""
env = DummyDiscretePixelEnv()
env = self._initialize_obs_env(env)
policy = CategoricalCNNPolicy(env=env,
kernel_sizes=kernel_sizes,
hidden_channels=hidden_channels,
strides=strides,
hidden_sizes=hidden_sizes)
env.reset()
obs, _, _, _ = env.step(1)
actions, _ = policy.get_actions([obs, obs, obs])
for action in actions:
assert env.action_space.contains(action)
torch_obs = torch.Tensor(obs)
actions, _ = policy.get_actions([torch_obs, torch_obs, torch_obs])
for action in actions:
assert env.action_space.contains(action)
def test_fire_reset(self):
env = DummyDiscretePixelEnv()
env_wrap = FireReset(env)
obs = env.reset()
obs_wrap = env_wrap.reset()
assert np.array_equal(obs, np.ones(env.observation_space.shape))
assert np.array_equal(obs_wrap, np.full(env.observation_space.shape,
2))
env_wrap.step(2)
obs_wrap = env_wrap.reset() # env will call reset again, after fire
assert np.array_equal(obs_wrap, np.ones(env.observation_space.shape))
def test_is_pickleable(self, hidden_channels, kernel_sizes, strides,
hidden_sizes):
"""Test if policy is pickable."""
env = DummyDiscretePixelEnv()
env = self._initialize_obs_env(env)
policy = CategoricalCNNPolicy(env=env,
kernel_sizes=kernel_sizes,
hidden_channels=hidden_channels,
strides=strides,
hidden_sizes=hidden_sizes)
env.reset()
obs, _, _, _ = env.step(1)
output_action_1, _ = policy.get_action(obs)
p = cloudpickle.dumps(policy)
policy_pickled = cloudpickle.loads(p)
output_action_2, _ = policy_pickled.get_action(obs)
assert env.action_space.contains(output_action_1)
assert env.action_space.contains(output_action_2)
assert output_action_1.shape == output_action_2.shape
