def test_get_actions(self, batch_size, hidden_sizes):
"""Test Tanh Gaussian Policy get actions function."""
env_spec = MetaRLEnv(DummyBoxEnv())
obs_dim = env_spec.observation_space.flat_dim
act_dim = env_spec.action_space.flat_dim
obs = torch.ones([batch_size, obs_dim], dtype=torch.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([batch_size, act_dim], 1.0)
action, prob = policy.get_actions(obs)
assert np.allclose(prob['mean'], expected_mean.numpy(), rtol=1e-3)
assert action.shape == (batch_size, act_dim)
def test_is_pickleable(self, batch_size, hidden_sizes):
"""Test if policy is unchanged after pickling."""
env_spec = MetaRLEnv(DummyBoxEnv())
obs_dim = env_spec.observation_space.flat_dim
obs = torch.ones([batch_size, obs_dim], dtype=torch.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_)
output1_action, output1_prob = policy.get_actions(obs)
p = pickle.dumps(policy)
policy_pickled = pickle.loads(p)
output2_action, output2_prob = policy_pickled.get_actions(obs)
assert np.allclose(output2_prob['mean'],
output1_prob['mean'],
rtol=1e-3)
assert output1_action.shape == output2_action.shape