def test_get_action(self, hidden_channels, kernel_sizes, strides,
hidden_sizes):
"""Test get_action function."""
env = GymEnv(DummyDiscretePixelEnv(), is_image=True)
policy = CategoricalCNNPolicy(env_spec=env.spec,
image_format='NHWC',
kernel_sizes=kernel_sizes,
hidden_channels=hidden_channels,
strides=strides,
hidden_sizes=hidden_sizes)
env.reset()
obs = env.step(1).observation
action, _ = policy.get_action(obs)
assert env.action_space.contains(action)
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_action_img_obs(self, hidden_channels, kernel_sizes, strides,
hidden_sizes):
"""Test get_action function with akro.Image observation space."""
env = GarageEnv(DummyDiscretePixelEnv(), is_image=True)
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_obs_unflattened(self, hidden_channels, kernel_sizes, strides,
hidden_sizes):
"""Test if a flattened image obs is passed to get_action
then it is unflattened.
"""
env = GymEnv(DummyDiscretePixelEnv(), is_image=True)
env.reset()
policy = CategoricalCNNPolicy(env_spec=env.spec,
image_format='NHWC',
kernel_sizes=kernel_sizes,
hidden_channels=hidden_channels,
strides=strides,
hidden_sizes=hidden_sizes)
obs = env.observation_space.sample()
action, _ = policy.get_action(env.observation_space.flatten(obs))
env.step(action)
def test_invalid_action_spaces(self):
"""Test that policy raises error if passed a box obs space."""
env = GymEnv(DummyDictEnv(act_space_type='box'))
with pytest.raises(ValueError):
CategoricalCNNPolicy(env=env,
kernel_sizes=(3, ),
hidden_channels=(3, ))
def test_does_not_support_dict_obs_space(self):
"""Test that policy raises error if passed a dict obs space."""
env = GymEnv(DummyDictEnv(act_space_type='discrete'))
with pytest.raises(ValueError,
match=('CNN policies do not support '
'with akro.Dict observation spaces.')):
CategoricalCNNPolicy(env=env,
kernel_sizes=(3, ),
hidden_channels=(3, ))
def test_get_actions(self, hidden_channels, kernel_sizes, strides,
hidden_sizes):
"""Test get_actions function with akro.Image observation space."""
env = GymEnv(DummyDiscretePixelEnv(), is_image=True)
policy = CategoricalCNNPolicy(env_spec=env.spec,
image_format='NHWC',
kernel_sizes=kernel_sizes,
hidden_channels=hidden_channels,
strides=strides,
hidden_sizes=hidden_sizes)
env.reset()
obs = env.step(1).observation
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_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_is_pickleable(self, hidden_channels, kernel_sizes, strides,
hidden_sizes):
"""Test if policy is pickable."""
env = GarageEnv(DummyDiscretePixelEnv(), is_image=True)
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
def test_is_pickleable(self, hidden_channels, kernel_sizes, strides,
hidden_sizes):
"""Test if policy is pickable."""
env = GymEnv(DummyDiscretePixelEnv(), is_image=True)
policy = CategoricalCNNPolicy(env_spec=env.spec,
image_format='NHWC',
kernel_sizes=kernel_sizes,
hidden_channels=hidden_channels,
strides=strides,
hidden_sizes=hidden_sizes)
env.reset()
obs = env.step(1).observation
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