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.compat.v1.placeholder(
tf.float32, shape=(None, None, env.observation_space.flat_dim))
with mock.patch(('metarl.tf.policies.'
'gaussian_lstm_policy.GaussianLSTMModel'),
new=SimpleGaussianLSTMModel):
policy = GaussianLSTMPolicy(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, mock_normal, obs_dim, action_dim, hidden_dim):
mock_normal.return_value = 0.5
env = TfEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('metarl.tf.policies.'
'gaussian_lstm_policy.GaussianLSTMModel'),
new=SimpleGaussianLSTMModel):
policy = GaussianLSTMPolicy(env_spec=env.spec,
state_include_action=False)
expected_action = np.full(action_dim, 0.5 * np.exp(0.5) + 0.5)
policy.reset()
obs = env.reset()
action, agent_info = policy.get_action(obs)
assert env.action_space.contains(action)
assert np.allclose(action,
np.full(action_dim, expected_action),
atol=1e-6)
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.allclose(action,
np.full(action_dim, expected_action),
atol=1e-6)
assert np.array_equal(mean, expected_mean)
assert np.array_equal(log_std, expected_log_std)
def test_gaussian_lstm_policy(self):
gaussian_lstm_policy = GaussianLSTMPolicy(env_spec=self.env,
hidden_dim=1)
self.sess.run(tf.compat.v1.global_variables_initializer())
gaussian_lstm_policy.reset()
obs = self.env.observation_space.high
assert gaussian_lstm_policy.get_action(obs)
def test_get_action(self, obs_dim, action_dim, hidden_dim):
env = MetaRLEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
obs_var = tf.compat.v1.placeholder(
tf.float32,
shape=[None, None, env.observation_space.flat_dim],
name='obs')
policy = GaussianLSTMPolicy(env_spec=env.spec,
hidden_dim=hidden_dim,
state_include_action=False)
policy.build(obs_var)
policy.reset()
obs = env.reset()
action, _ = policy.get_action(obs.flatten())
assert env.action_space.contains(action)
actions, _ = policy.get_actions([obs.flatten()])
for action in actions:
assert env.action_space.contains(action)
def test_dist_info_sym_wrong_input(self):
env = TfEnv(DummyBoxEnv(obs_dim=(1, ), action_dim=(1, )))
obs_ph = tf.compat.v1.placeholder(
tf.float32, shape=(None, None, env.observation_space.flat_dim))
with mock.patch(('metarl.tf.policies.'
'gaussian_lstm_policy.GaussianLSTMModel'),
new=SimpleGaussianLSTMModel):
policy = GaussianLSTMPolicy(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 pytest.raises(tf.errors.InvalidArgumentError):
self.sess.run(
policy.model.networks['p2_sym'].input,
feed_dict={obs_ph: [[obs.flatten()], [obs.flatten()]]})
