def test_get_embedding(self, obs_dim, embedding_dim):
env = GarageEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=embedding_dim))
embedding_spec = InOutSpec(input_space=env.spec.observation_space,
output_space=env.spec.action_space)
embedding = GaussianMLPEncoder(embedding_spec)
env.reset()
obs, _, _, _ = env.step(1)
latent, _ = embedding.forward(obs)
assert env.action_space.contains(latent)
def test_get_embedding(self, obs_dim, embedding_dim):
env = GarageEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=embedding_dim))
embedding_spec = InOutSpec(input_space=env.spec.observation_space,
output_space=env.spec.action_space)
embedding = GaussianMLPEncoder(embedding_spec)
task_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, None,
embedding.input_dim))
embedding.build(task_input)
env.reset()
obs, _, _, _ = env.step(1)
latent, _ = embedding.forward(obs)
assert env.action_space.contains(latent)
def test_get_embedding(self, mock_normal, obs_dim, embedding_dim):
mock_normal.return_value = 0.5
env = TfEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=embedding_dim))
with mock.patch(('garage.tf.embeddings.'
'gaussian_mlp_encoder.GaussianMLPModel'),
new=SimpleGaussianMLPModel):
embedding_spec = InOutSpec(input_space=env.spec.observation_space,
output_space=env.spec.action_space)
embedding = GaussianMLPEncoder(embedding_spec)
env.reset()
obs, _, _, _ = env.step(1)
latent, prob = embedding.forward(obs)
expected_embedding = np.full(embedding_dim, 0.75)
expected_mean = np.full(embedding_dim, 0.5)
expected_log_std = np.full(embedding_dim, np.log(0.5))
assert env.action_space.contains(latent)
assert np.array_equal(latent, expected_embedding)
assert np.array_equal(prob['mean'], expected_mean)
assert np.array_equal(prob['log_std'], expected_log_std)