def test_get_actions(self, action_dim, kernel_sizes, hidden_channels,
strides, paddings):
"""Test get_actions function."""
batch_size = 64
input_width = 32
input_height = 32
in_channel = 3
input_shape = (batch_size, in_channel, input_height, input_width)
env = GymEnv(
DummyDiscreteEnv(obs_dim=input_shape, action_dim=action_dim))
env = self._initialize_obs_env(env)
policy = DiscreteCNNPolicy(env_spec=env.spec,
hidden_channels=hidden_channels,
hidden_sizes=hidden_channels,
kernel_sizes=kernel_sizes,
strides=strides,
paddings=paddings,
padding_mode='zeros',
hidden_w_init=nn.init.ones_,
output_w_init=nn.init.ones_)
env.reset()
obs = env.step(1).observation
actions, _ = policy.get_actions([obs, obs, obs])
for action in actions:
assert env.action_space.contains(int(action[0]))
assert env.action_space.n == action_dim
def test_obs_unflattened(self, action_dim, kernel_sizes, hidden_channels,
strides, paddings):
"""Test if a flattened image obs is passed to get_action
then it is unflattened.
"""
batch_size = 64
input_width = 32
input_height = 32
in_channel = 3
input_shape = (batch_size, in_channel, input_height, input_width)
env = GymEnv(
DummyDiscreteEnv(obs_dim=input_shape, action_dim=action_dim))
env = self._initialize_obs_env(env)
env.reset()
policy = DiscreteCNNPolicy(env_spec=env.spec,
hidden_channels=hidden_channels,
hidden_sizes=hidden_channels,
kernel_sizes=kernel_sizes,
strides=strides,
paddings=paddings,
padding_mode='zeros',
hidden_w_init=nn.init.ones_,
output_w_init=nn.init.ones_)
obs = env.observation_space.sample()
action, _ = policy.get_action(env.observation_space.flatten(obs))
env.step(action)
def test_is_pickleable(self, action_dim, kernel_sizes, hidden_channels,
strides, paddings):
"""Test if policy is pickable."""
batch_size = 64
input_width = 32
input_height = 32
in_channel = 3
input_shape = (batch_size, in_channel, input_height, input_width)
env = GymEnv(
DummyDiscreteEnv(obs_dim=input_shape, action_dim=action_dim))
env = self._initialize_obs_env(env)
policy = DiscreteCNNPolicy(env_spec=env.spec,
hidden_channels=hidden_channels,
hidden_sizes=hidden_channels,
kernel_sizes=kernel_sizes,
strides=strides,
paddings=paddings,
padding_mode='zeros',
hidden_w_init=nn.init.ones_,
output_w_init=nn.init.ones_)
env.reset()
obs = env.step(1).observation
output_action_1, _ = policy.get_action(obs.flatten())
p = cloudpickle.dumps(policy)
policy_pickled = cloudpickle.loads(p)
output_action_2, _ = policy_pickled.get_action(obs)
assert env.action_space.contains(int(output_action_1[0]))
assert env.action_space.contains(int(output_action_2[0]))
assert output_action_1.shape == output_action_2.shape
def test_is_pickleable(self, kernel_sizes, hidden_channels, strides,
paddings):
"""Test if policy is pickable."""
env = GymEnv(DummyDiscretePixelEnv())
policy = DiscreteCNNPolicy(env_spec=env.spec,
image_format='NHWC',
hidden_channels=hidden_channels,
kernel_sizes=kernel_sizes,
strides=strides,
paddings=paddings,
padding_mode='zeros',
hidden_w_init=nn.init.ones_,
output_w_init=nn.init.ones_)
env.reset()
obs = env.step(1).observation
output_action_1, _ = policy.get_action(obs.flatten())
p = cloudpickle.dumps(policy)
policy_pickled = cloudpickle.loads(p)
output_action_2, _ = policy_pickled.get_action(obs)
assert env.action_space.contains(int(output_action_1[0]))
assert env.action_space.contains(int(output_action_2[0]))
assert output_action_1.shape == output_action_2.shape
def test_get_action(self, kernel_sizes, hidden_channels, strides,
paddings):
"""Test get_action function."""
env = GymEnv(DummyDiscretePixelEnv())
policy = DiscreteCNNPolicy(env_spec=env.spec,
image_format='NHWC',
hidden_channels=hidden_channels,
kernel_sizes=kernel_sizes,
strides=strides,
paddings=paddings,
padding_mode='zeros',
hidden_w_init=nn.init.ones_,
output_w_init=nn.init.ones_)
env.reset()
obs = env.step(1).observation
action, _ = policy.get_action(obs.flatten())
assert env.action_space.contains(int(action[0]))
def test_obs_unflattened(self, kernel_sizes, hidden_channels, strides,
paddings):
"""Test if a flattened image obs is passed to get_action
then it is unflattened.
"""
env = GymEnv(DummyDiscretePixelEnv())
env.reset()
policy = DiscreteCNNPolicy(env_spec=env.spec,
image_format='NHWC',
hidden_channels=hidden_channels,
kernel_sizes=kernel_sizes,
strides=strides,
paddings=paddings,
padding_mode='zeros',
hidden_w_init=nn.init.ones_,
output_w_init=nn.init.ones_)
obs = env.observation_space.sample()
action, _ = policy.get_action(env.observation_space.flatten(obs))
env.step(action[0])