def test_dist_info_sym_include_action(self, obs_dim, action_dim,
hidden_dim):
env = TfEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
obs_ph = tf.placeholder(
tf.float32, shape=(None, None, env.observation_space.flat_dim))
with mock.patch(('garage.tf.policies.'
'gaussian_lstm_policy_with_model.GaussianLSTMModel'),
new=SimpleGaussianLSTMModel):
policy = GaussianLSTMPolicyWithModel(
env_spec=env.spec, state_include_action=True)
policy.reset()
obs = env.reset()
dist_sym = policy.dist_info_sym(
obs_var=obs_ph,
state_info_vars={'prev_action': np.zeros((2, 1) + action_dim)},
name='p2_sym')
dist = self.sess.run(
dist_sym, feed_dict={obs_ph: [[obs.flatten()], [obs.flatten()]]})
assert np.array_equal(dist['mean'], np.full((2, 1) + action_dim, 0.5))
assert np.array_equal(dist['log_std'], np.full((2, 1) + action_dim,
0.5))
def test_get_action(self, obs_dim, action_dim, hidden_dim):
env = TfEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'gaussian_lstm_policy_with_model.GaussianLSTMModel'),
new=SimpleGaussianLSTMModel):
policy = GaussianLSTMPolicyWithModel(
env_spec=env.spec, state_include_action=False)
policy.reset()
obs = env.reset()
action, agent_info = policy.get_action(obs)
assert env.action_space.contains(action)
assert np.array_equal(action, np.full(action_dim, 0.75))
expected_mean = np.full(action_dim, 0.5)
assert np.array_equal(agent_info['mean'], expected_mean)
expected_log_std = np.full(action_dim, 0.5)
assert np.array_equal(agent_info['log_std'], expected_log_std)
actions, agent_infos = policy.get_actions([obs])
for action, mean, log_std in zip(actions, agent_infos['mean'],
agent_infos['log_std']):
assert env.action_space.contains(action)
assert np.array_equal(action, np.full(action_dim, 0.75))
assert np.array_equal(mean, expected_mean)
assert np.array_equal(log_std, expected_log_std)
def test_dist_info_sym_wrong_input(self):
env = TfEnv(DummyBoxEnv(obs_dim=(1, ), action_dim=(1, )))
obs_ph = tf.placeholder(tf.float32,
shape=(None, None,
env.observation_space.flat_dim))
with mock.patch(('garage.tf.policies.'
'gaussian_lstm_policy_with_model.GaussianLSTMModel'),
new=SimpleGaussianLSTMModel):
policy = GaussianLSTMPolicyWithModel(env_spec=env.spec,
state_include_action=True)
policy.reset()
obs = env.reset()
policy.dist_info_sym(
obs_var=obs_ph,
state_info_vars={'prev_action': np.zeros((3, 1, 1))},
name='p2_sym')
# observation batch size = 2 but prev_action batch size = 3
with self.assertRaises(tf.errors.InvalidArgumentError):
self.sess.run(
policy.model.networks['p2_sym'].input,
feed_dict={obs_ph: [[obs.flatten()], [obs.flatten()]]})
class TestGaussianLSTMPolicyWithModelTransit(TfGraphTestCase):
def setup_method(self):
super().setup_method()
with mock.patch('tensorflow.random.normal') as mock_rand:
mock_rand.return_value = 0.5
env = TfEnv(DummyBoxEnv(obs_dim=(1, ), action_dim=(1, )))
self.default_initializer = tf.constant_initializer(1)
self.default_hidden_nonlinearity = tf.nn.tanh
self.default_recurrent_nonlinearity = tf.nn.sigmoid
self.default_output_nonlinearity = None
self.time_step = 1
self.policy1 = GaussianLSTMPolicy(
env_spec=env.spec,
hidden_dim=4,
hidden_nonlinearity=self.default_hidden_nonlinearity,
recurrent_nonlinearity=self.default_recurrent_nonlinearity,
recurrent_w_x_init=self.default_initializer,
recurrent_w_h_init=self.default_initializer,
output_nonlinearity=self.default_output_nonlinearity,
output_w_init=self.default_initializer,
state_include_action=True,
name='P1')
self.policy2 = GaussianLSTMPolicy(
env_spec=env.spec,
hidden_dim=4,
hidden_nonlinearity=self.default_hidden_nonlinearity,
recurrent_nonlinearity=self.default_recurrent_nonlinearity,
recurrent_w_x_init=self.default_initializer,
recurrent_w_h_init=self.default_initializer,
output_nonlinearity=self.default_output_nonlinearity,
output_w_init=tf.constant_initializer(2),
state_include_action=True,
name='P2')
self.sess.run(tf.global_variables_initializer())
self.policy3 = GaussianLSTMPolicyWithModel(
env_spec=env.spec,
hidden_dim=4,
hidden_nonlinearity=self.default_hidden_nonlinearity,
hidden_w_init=self.default_initializer,
recurrent_nonlinearity=self.default_recurrent_nonlinearity,
recurrent_w_init=self.default_initializer,
output_nonlinearity=self.default_output_nonlinearity,
output_w_init=self.default_initializer,
state_include_action=True,
name='P3')
self.policy4 = GaussianLSTMPolicyWithModel(
env_spec=env.spec,
hidden_dim=4,
hidden_nonlinearity=self.default_hidden_nonlinearity,
hidden_w_init=self.default_initializer,
recurrent_nonlinearity=self.default_recurrent_nonlinearity,
recurrent_w_init=self.default_initializer,
output_nonlinearity=self.default_output_nonlinearity,
output_w_init=tf.constant_initializer(2),
state_include_action=True,
name='P4')
self.policy1.reset()
self.policy2.reset()
self.policy3.reset()
self.policy4.reset()
self.obs = [env.reset()]
self.obs = np.concatenate(
[self.obs for _ in range(self.time_step)], axis=0)
self.obs_ph = tf.placeholder(
tf.float32, shape=(None, None, env.observation_space.flat_dim))
self.action_ph = tf.placeholder(tf.float32,
shape=(None, None,
env.action_space.flat_dim))
self.dist1_sym = self.policy1.dist_info_sym(
obs_var=self.obs_ph,
state_info_vars={
'prev_action': np.zeros((2, self.time_step, 1))
},
name='p1_sym')
self.dist2_sym = self.policy2.dist_info_sym(
obs_var=self.obs_ph,
state_info_vars={
'prev_action': np.zeros((2, self.time_step, 1))
},
name='p2_sym')
self.dist3_sym = self.policy3.dist_info_sym(
obs_var=self.obs_ph,
state_info_vars={
'prev_action': np.zeros((2, self.time_step, 1))
},
name='p3_sym')
self.dist4_sym = self.policy4.dist_info_sym(
obs_var=self.obs_ph,
state_info_vars={
'prev_action': np.zeros((2, self.time_step, 1))
},
name='p4_sym')
def test_dist_info_sym_output(self):
# batch size = 2
dist1 = self.sess.run(self.dist1_sym,
feed_dict={self.obs_ph: [self.obs, self.obs]})
dist2 = self.sess.run(self.dist2_sym,
feed_dict={self.obs_ph: [self.obs, self.obs]})
dist3 = self.sess.run(self.dist3_sym,
feed_dict={self.obs_ph: [self.obs, self.obs]})
dist4 = self.sess.run(self.dist4_sym,
feed_dict={self.obs_ph: [self.obs, self.obs]})
assert np.array_equal(dist1['mean'], dist3['mean'])
assert np.array_equal(dist1['log_std'], dist3['log_std'])
assert np.array_equal(dist2['mean'], dist4['mean'])
assert np.array_equal(dist2['log_std'], dist4['log_std'])
@mock.patch('numpy.random.normal')
def test_get_action(self, mock_rand):
mock_rand.return_value = 0.5
action1, agent_info1 = self.policy1.get_action(self.obs)
action2, agent_info2 = self.policy2.get_action(self.obs)
action3, agent_info3 = self.policy3.get_action(self.obs)
action4, agent_info4 = self.policy4.get_action(self.obs)
assert np.array_equal(action1, action3)
assert np.array_equal(action2, action4)
assert np.array_equal(agent_info1['mean'], agent_info3['mean'])
assert np.array_equal(agent_info1['log_std'], agent_info3['log_std'])
assert np.array_equal(agent_info2['mean'], agent_info4['mean'])
assert np.array_equal(agent_info2['log_std'], agent_info4['log_std'])
actions1, agent_infos1 = self.policy1.get_actions([self.obs])
actions2, agent_infos2 = self.policy2.get_actions([self.obs])
actions3, agent_infos3 = self.policy3.get_actions([self.obs])
actions4, agent_infos4 = self.policy4.get_actions([self.obs])
assert np.array_equal(actions1, actions3)
assert np.array_equal(actions2, actions4)
assert np.array_equal(agent_infos1['mean'], agent_infos3['mean'])
assert np.array_equal(agent_infos1['log_std'], agent_infos3['log_std'])
assert np.array_equal(agent_infos2['mean'], agent_infos4['mean'])
assert np.array_equal(agent_infos2['log_std'], agent_infos4['log_std'])
def test_kl_sym(self):
kl_diff_sym1 = self.policy1.distribution.kl_sym(
self.dist1_sym, self.dist2_sym)
objective1 = tf.reduce_mean(kl_diff_sym1)
kl_func = tensor_utils.compile_function([self.obs_ph], objective1)
kl1 = kl_func([self.obs, self.obs])
kl_diff_sym2 = self.policy3.distribution.kl_sym(
self.dist3_sym, self.dist4_sym)
objective2 = tf.reduce_mean(kl_diff_sym2)
kl_func = tensor_utils.compile_function([self.obs_ph], objective2)
kl2 = kl_func([self.obs, self.obs])
assert np.array_equal(kl1, kl2)
def test_log_likehihood_sym(self):
log_prob_sym1 = self.policy1.distribution.log_likelihood_sym(
self.action_ph, self.dist1_sym)
log_prob_func = tensor_utils.compile_function(
[self.obs_ph, self.action_ph], log_prob_sym1)
log_prob1 = log_prob_func([self.obs, self.obs],
np.ones((2, self.time_step, 1)))
log_prob_sym2 = self.policy3.distribution.log_likelihood_sym(
self.action_ph, self.dist3_sym)
log_prob_func2 = tensor_utils.compile_function(
[self.obs_ph, self.action_ph], log_prob_sym2)
log_prob2 = log_prob_func2([self.obs, self.obs],
np.ones((2, self.time_step, 1)))
assert np.array_equal(log_prob1, log_prob2)
log_prob_sym1 = self.policy2.distribution.log_likelihood_sym(
self.action_ph, self.dist2_sym)
log_prob_func = tensor_utils.compile_function(
[self.obs_ph, self.action_ph], log_prob_sym1)
log_prob1 = log_prob_func([self.obs, self.obs],
np.ones((2, self.time_step, 1)))
log_prob_sym2 = self.policy4.distribution.log_likelihood_sym(
self.action_ph, self.dist4_sym)
log_prob_func2 = tensor_utils.compile_function(
[self.obs_ph, self.action_ph], log_prob_sym2)
log_prob2 = log_prob_func2([self.obs, self.obs],
np.ones((2, self.time_step, 1)))
assert np.array_equal(log_prob1, log_prob2)
def test_policy_entropy_sym(self):
entropy_sym1 = self.policy1.distribution.entropy_sym(
self.dist1_sym, name='entropy_sym1')
entropy_func = tensor_utils.compile_function([self.obs_ph],
entropy_sym1)
entropy1 = entropy_func([self.obs, self.obs])
entropy_sym2 = self.policy3.distribution.entropy_sym(
self.dist3_sym, name='entropy_sym1')
entropy_func = tensor_utils.compile_function([self.obs_ph],
entropy_sym2)
entropy2 = entropy_func([self.obs, self.obs])
assert np.array_equal(entropy1, entropy2)
def test_likelihood_ratio_sym(self):
likelihood_ratio_sym1 = self.policy1.distribution.likelihood_ratio_sym(
self.action_ph,
self.dist1_sym,
self.dist2_sym,
name='li_ratio_sym1')
likelihood_ratio_func = tensor_utils.compile_function(
[self.action_ph, self.obs_ph], likelihood_ratio_sym1)
likelihood_ratio1 = likelihood_ratio_func(np.ones((2, 1, 1)),
[self.obs, self.obs])
likelihood_ratio_sym2 = self.policy3.distribution.likelihood_ratio_sym(
self.action_ph,
self.dist3_sym,
self.dist4_sym,
name='li_ratio_sym2')
likelihood_ratio_func = tensor_utils.compile_function(
[self.action_ph, self.obs_ph], likelihood_ratio_sym2)
likelihood_ratio2 = likelihood_ratio_func(np.ones((2, 1, 1)),
[self.obs, self.obs])
assert np.array_equal(likelihood_ratio1, likelihood_ratio2)