def test_get_action_dict_space(self):
"""Test if observations from dict obs spaces are properly flattened."""
env = GymEnv(DummyDictEnv(obs_space_type='box', act_space_type='box'))
policy = TanhGaussianMLPPolicy(env_spec=env.spec,
hidden_nonlinearity=None,
hidden_sizes=(1, ),
hidden_w_init=nn.init.ones_,
output_w_init=nn.init.ones_)
obs = env.reset()[0]
action, _ = policy.get_action(obs)
assert env.action_space.shape == action.shape
actions, _ = policy.get_actions(np.array([obs, obs]))
for action in actions:
assert env.action_space.shape == action.shape
def test_get_action_np(self, hidden_sizes):
"""Test Policy get action function with numpy inputs."""
env_spec = GymEnv(DummyBoxEnv())
obs_dim = env_spec.observation_space.flat_dim
act_dim = env_spec.action_space.flat_dim
obs = np.ones((obs_dim, ), dtype=np.float32)
init_std = 2.
policy = TanhGaussianMLPPolicy(env_spec=env_spec,
hidden_sizes=hidden_sizes,
init_std=init_std,
hidden_nonlinearity=None,
std_parameterization='exp',
hidden_w_init=nn.init.ones_,
output_w_init=nn.init.ones_)
expected_mean = torch.full((act_dim, ), 1.0, dtype=torch.float)
action, prob = policy.get_action(obs)
assert np.allclose(prob['mean'], expected_mean.numpy(), rtol=1e-3)
assert action.shape == (act_dim, )
