def test_is_pickleable(self, obs_dim, action_dim, mock_rand):
mock_rand.return_value = 0
env = TfEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'categorical_mlp_policy_with_model.MLPModel'),
new=SimpleMLPModel):
policy = CategoricalMLPPolicyWithModel(env_spec=env.spec)
env.reset()
obs, _, _, _ = env.step(1)
expected_prob = np.full(action_dim, 0.5)
p = pickle.dumps(policy)
with tf.Session(graph=tf.Graph()):
policy_pickled = pickle.loads(p)
action, prob = policy_pickled.get_action(obs)
assert env.action_space.contains(action)
assert action == 0
assert np.array_equal(prob['prob'], expected_prob)
prob1 = policy.dist_info([obs.flatten()])
prob2 = policy_pickled.dist_info([obs.flatten()])
assert np.array_equal(prob1['prob'], prob2['prob'])
assert np.array_equal(prob2['prob'][0], expected_prob)
def test_dist_info(self, obs_dim, action_dim):
env = TfEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'categorical_mlp_policy_with_model.MLPModel'),
new=SimpleMLPModel):
policy = CategoricalMLPPolicyWithModel(env_spec=env.spec)
env.reset()
obs, _, _, _ = env.step(1)
expected_prob = np.full(action_dim, 0.5)
policy_probs = policy.dist_info([obs.flatten()])
assert np.array_equal(policy_probs['prob'][0], expected_prob)
def test_is_pickleable(self, obs_dim, action_dim):
env = TfEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'categorical_mlp_policy_with_model.MLPModel'),
new=SimpleMLPModel):
policy = CategoricalMLPPolicyWithModel(env_spec=env.spec)
env.reset()
obs, _, _, _ = env.step(1)
with tf.variable_scope('CategoricalMLPPolicy/MLPModel', reuse=True):
return_var = tf.get_variable('return_var')
# assign it to all one
return_var.load(tf.ones_like(return_var).eval())
output1 = self.sess.run(
policy.model.outputs,
feed_dict={policy.model.input: [obs.flatten()]})
p = pickle.dumps(policy)
with tf.Session(graph=tf.Graph()) as sess:
policy_pickled = pickle.loads(p)
output2 = sess.run(
policy_pickled.model.outputs,
feed_dict={policy_pickled.model.input: [obs.flatten()]})
assert np.array_equal(output1, output2)
def test_dist_info_sym(self, obs_dim, action_dim):
env = TfEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'categorical_mlp_policy_with_model.MLPModel'),
new=SimpleMLPModel):
policy = CategoricalMLPPolicyWithModel(env_spec=env.spec)
env.reset()
obs, _, _, _ = env.step(1)
expected_prob = np.full(action_dim, 0.5)
obs_dim = env.spec.observation_space.flat_dim
state_input = tf.placeholder(tf.float32, shape=(None, obs_dim))
dist1 = policy.dist_info_sym(state_input, name='policy2')
prob = self.sess.run(
dist1['prob'], feed_dict={state_input: [obs.flatten()]})
assert np.array_equal(prob[0], expected_prob)
def test_get_action(self, mock_rand, obs_dim, action_dim):
mock_rand.return_value = 0
env = TfEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'categorical_mlp_policy_with_model.MLPModel'),
new=SimpleMLPModel):
policy = CategoricalMLPPolicyWithModel(env_spec=env.spec)
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)
