def load_pearl(env_name="CartPole-v0"):
"""Return an instance of the PEARL algorithm.
NOTE: currently not working.
"""
num_train_tasks = 100
num_test_tasks = 30
latent_size = 5
net_size = 300
encoder_hidden_size = 200
encoder_hidden_sizes = (encoder_hidden_size, encoder_hidden_size,
encoder_hidden_size)
# Create multi-task environment and sample tasks.
env_start = GarageEnv(env_name=env_name)
env_sampler = SetTaskSampler(lambda: GarageEnv(normalize(env_start)))
env = env_sampler.sample(num_train_tasks)
test_env_sampler = SetTaskSampler(lambda: GarageEnv(normalize(env_start)))
# Instantiate networks.
augmented_env = PEARL.augment_env_spec(env[0](), latent_size)
qf = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), latent_size, 'vf')
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(env=env,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=num_train_tasks,
num_test_tasks=num_test_tasks,
latent_dim=latent_size,
encoder_hidden_sizes=encoder_hidden_sizes,
test_env_sampler=test_env_sampler)
return pearl
