def test_get_action(self, mock_rand, obs_dim, action_dim, filter_dims,
filter_sizes, strides, padding, hidden_sizes):
mock_rand.return_value = 0
env = TfEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'categorical_cnn_policy.MLPModel'),
new=SimpleMLPModel):
with mock.patch(('garage.tf.policies.'
'categorical_cnn_policy.CNNModel'),
new=SimpleCNNModel):
policy = CategoricalCNNPolicy(env_spec=env.spec,
conv_filters=filter_dims,
conv_filter_sizes=filter_sizes,
conv_strides=strides,
conv_pad=padding,
hidden_sizes=hidden_sizes)
env.reset()
obs, _, _, _ = env.step(1)
action, prob = policy.get_action(obs)
expected_prob = np.full(action_dim, 0.5)
assert env.action_space.contains(action)
assert action == 0
assert np.array_equal(prob['prob'], expected_prob)
actions, probs = policy.get_actions([obs, obs, obs])
for action, prob in zip(actions, probs['prob']):
assert env.action_space.contains(action)
assert action == 0
assert np.array_equal(prob, expected_prob)
def test_get_action(self, filters, strides, padding, hidden_sizes):
env = GymEnv(DummyDiscretePixelEnv())
policy = CategoricalCNNPolicy(env_spec=env.spec,
filters=filters,
strides=strides,
padding=padding,
hidden_sizes=hidden_sizes)
env.reset()
obs = env.step(1).observation
action, _ = policy.get_action(obs)
assert env.action_space.contains(action)
actions, _ = policy.get_actions([obs, obs, obs])
for action in actions:
assert env.action_space.contains(action)
class TestCategoricalCNNPolicyImageObs(TfGraphTestCase):
def setup_method(self):
super().setup_method()
self.env = GymEnv(DummyDiscretePixelEnv(), is_image=True)
self.sess.run(tf.compat.v1.global_variables_initializer())
self.env.reset()
@pytest.mark.parametrize('filters, strides, padding, hidden_sizes', [
(((3, (32, 32)), ), (1, ), 'VALID', (4, )),
])
def test_obs_unflattened(self, filters, strides, padding, hidden_sizes):
self.policy = CategoricalCNNPolicy(env_spec=self.env.spec,
filters=filters,
strides=strides,
padding=padding,
hidden_sizes=hidden_sizes)
obs = self.env.observation_space.sample()
action, _ = self.policy.get_action(
self.env.observation_space.flatten(obs))
self.env.step(action)
def test_get_action(self, filters, strides, padding, hidden_sizes):
env = GarageEnv(DummyDiscretePixelEnv())
policy = CategoricalCNNPolicy(env_spec=env.spec,
filters=filters,
strides=strides,
padding=padding,
hidden_sizes=hidden_sizes)
obs_var = tf.compat.v1.placeholder(tf.float32,
shape=(None, None) +
env.observation_space.shape,
name='obs')
policy.build(obs_var)
env.reset()
obs, _, _, _ = env.step(1)
action, _ = policy.get_action(obs)
assert env.action_space.contains(action)
actions, _ = policy.get_actions([obs, obs, obs])
for action in actions:
assert env.action_space.contains(action)
