def test_maml_sampling(self):
# get from data
# get from data
env = PointEnv()
paths = sample_random_trajectories_point_env(env, num_paths=100, horizon=100)
dynamics_model = MLPDynamicsEnsemble("dyn_model3", env, hidden_sizes=(16,16), num_models=4)
obs = np.concatenate([path['observations'] for path in paths], axis=0)
obs_next = np.concatenate([path['next_observations'] for path in paths], axis=0)
act = np.concatenate([path['actions'] for path in paths], axis=0)
env = TfEnv(normalize(PointEnv()))
policy = MAMLImprovedGaussianMLPPolicy(
name="policy3",
env_spec=env.spec,
hidden_sizes=(100, 100),
grad_step_size=0.1,
hidden_nonlinearity=tf.nn.tanh,
trainable_step_size=False,
bias_transform=False
)
from rllab_maml.baselines.linear_feature_baseline import LinearFeatureBaseline
baseline = LinearFeatureBaseline(env_spec=env.spec)
# fit dynamics model
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
dynamics_model.fit(obs, act, obs_next, epochs=1)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=20000,
max_path_length=100,
n_itr=10,
discount=0.99,
step_size=0.01,
)
algo.meta_batch_size = dynamics_model.num_models
algo.batch_size_dynamics_samples = algo.batch_size
algo.dynamics_model = dynamics_model
itr = 1
model_sampler = MAMLModelVectorizedSampler(algo)
model_sampler.start_worker()
paths = model_sampler.obtain_samples(itr, return_dict=True)
samples_data = model_sampler.process_samples(itr, paths[0])
print(samples_data.keys())
def test_model_sampling_with_dummy_different_meta_batch_size(self):
env = DummyEnv()
dynamics_dummy = DummyDynamicsEnsemble("dyn_model4", env, num_models=4)
env = TfEnv(normalize(DummyEnv()))
policy = MAMLImprovedGaussianMLPPolicy(
name="policy4",
env_spec=env.spec,
hidden_sizes=(100, 100),
grad_step_size=0.1,
hidden_nonlinearity=tf.nn.tanh,
trainable_step_size=False,
bias_transform=False
)
from rllab_maml.baselines.linear_feature_baseline import LinearFeatureBaseline
baseline = LinearFeatureBaseline(env_spec=env.spec)
# fit dynamics model
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=20000,
max_path_length=100,
n_itr=10,
discount=0.99,
step_size=0.01,
)
algo.meta_batch_size = dynamics_dummy.num_models * 2
algo.batch_size_dynamics_samples = algo.batch_size
algo.dynamics_model = dynamics_dummy
itr = 1
model_sampler = MAMLModelVectorizedSampler(algo)
model_sampler.start_worker()
paths = model_sampler.obtain_samples(itr, return_dict=True)
n_steps_per_model = np.array(
[np.sum([path['observations'].shape[0] for path in model_paths]) for model_paths in paths.values()])
self.assertTrue(
all(np.abs(n_steps_per_model - algo.batch_size // algo.meta_batch_size) <= algo.max_path_length))
for i in range(dynamics_dummy.num_models):
for path in paths[i]:
self.assertTrue(
(np.logical_or(path['observations'] == 1.0, path['observations'] == i//2 * 0.01)).all())
def test_model_sampling_with_given_traj_starting_obs(self):
env = DummyEnv()
dynamics_dummy = DummyDynamicsEnsemble("dyn_model4", env, num_models=4)
env = TfEnv(normalize(DummyEnv()))
policy = MAMLImprovedGaussianMLPPolicy(
name="policy4",
env_spec=env.spec,
hidden_sizes=(100, 100),
grad_step_size=0.1,
hidden_nonlinearity=tf.nn.tanh,
trainable_step_size=False,
bias_transform=False
)
from rllab_maml.baselines.linear_feature_baseline import LinearFeatureBaseline
baseline = LinearFeatureBaseline(env_spec=env.spec)
# fit dynamics model
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=20000,
max_path_length=100,
n_itr=10,
discount=0.99,
step_size=0.01,
)
algo.meta_batch_size = dynamics_dummy.num_models * 2
algo.batch_size_dynamics_samples = algo.batch_size
algo.dynamics_model = dynamics_dummy
itr = 1
model_sampler = MAMLModelVectorizedSampler(algo)
model_sampler.start_worker()
traj_starting_obs = np.array([[-1, -1],[-0.5, -0.5]])
paths = model_sampler.obtain_samples(itr, return_dict=True, traj_starting_obs=traj_starting_obs)
for i in range(dynamics_dummy.num_models):
for path in paths[i]:
print(path['observations'][0])
print(np.abs(np.mean(path['observations'][0]) + 1.0) < 0.001)
self.assertTrue(
np.abs(np.mean(path['observations'][0]) + 1.0) < 0.001 or np.abs(np.mean(path['observations'][0])+0.5) < 0.001)