def test_param_space_noise(self):
env = TfEnv(normalize(PointEnvMAML()))
obs = env.reset()
policy = MAMLImprovedGaussianMLPPolicy(name="policy33",
env_spec=env.spec,
hidden_sizes=(16, 16),
hidden_nonlinearity=tf.nn.tanh,
param_noise_std=0.0)
baseline = LinearFeatureBaseline(env_spec=env.spec)
import rllab.misc.logger as logger
logger.set_snapshot_dir('/tmp/')
logger.set_snapshot_mode('last')
algo = MAMLTRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=3,
max_path_length=10,
meta_batch_size=4,
num_grad_updates=1,
n_itr=1,
discount=0.99,
step_size=0.01,
)
algo.train()
tf.reset_default_graph()
pkl_file = os.path.join('/tmp/', 'params.pkl')
with tf.Session() as sess:
data = joblib.load(pkl_file)
policy = data['policy']
action_1 = policy.get_action(obs)[1]['mean']
action_2 = policy.get_action(obs)[1]['mean']
diff = np.sum((action_1 - action_2)**2)
self.assertAlmostEquals(diff, 0.0)
action_1 = policy.get_action(obs, param_noise_std=1.0)[1]['mean']
action_2 = policy.get_action(obs, param_noise_std=1.0)[1]['mean']
diff = np.sum((action_1 - action_2)**2)
self.assertGreaterEqual(diff, 0.1)
policy.param_noise_std = 1.0
action_1 = policy.get_action(obs)[1]['mean']
action_2 = policy.get_action(obs)[1]['mean']
diff = np.sum((action_1 - action_2)**2)
self.assertGreaterEqual(diff, 0.1)
def test_serialization(self):
env = TfEnv(normalize(PointEnvMAML()))
obs = env.reset()
policy = MAMLImprovedGaussianMLPPolicy(
name="policy56",
env_spec=env.spec,
hidden_sizes=(16, 16),
hidden_nonlinearity=tf.nn.tanh,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
import rllab.misc.logger as logger
logger.set_snapshot_dir('/tmp/')
logger.set_snapshot_mode('last')
algo = MAMLTRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=2,
max_path_length=10,
meta_batch_size=4,
num_grad_updates=1,
n_itr=1,
discount=0.99,
step_size=0.01,
)
algo.train()
tf.reset_default_graph()
pkl_file = os.path.join('/tmp/', 'params.pkl')
with tf.Session() as sess:
data = joblib.load(pkl_file)
policy = data['policy']
action_before = policy.get_action(obs)[1]['mean']
dump_string = pickle.dumps(policy)
tf.reset_default_graph()
with tf.Session() as sess:
policy_loaded = pickle.loads(dump_string)
action_after = policy_loaded.get_action(obs)[1]['mean']
diff = np.sum(np.abs(action_before - action_after))
self.assertAlmostEquals(diff, 0.0, places=3)
def test_get_mean_stepsize(self):
env = TfEnv(normalize(PointEnvMAML()))
obs = env.reset()
policy = MAMLImprovedGaussianMLPPolicy(name="policy2",
env_spec=env.spec,
hidden_sizes=(16, 16),
hidden_nonlinearity=tf.nn.tanh,
trainable_step_size=True,
grad_step_size=0.7)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
mean_stepsize_1 = policy.get_mean_step_size()
self.assertAlmostEquals(mean_stepsize_1, 0.7, places=5)