def test_maml_trpo_dummy_named_env():
"""Test with dummy environment that has env_name."""
env = GarageEnv(
normalize(DummyMultiTaskBoxEnv(), expected_action_scale=10.))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32))
rollouts_per_task = 2
max_episode_length = 100
runner = LocalRunner(snapshot_config)
algo = MAMLTRPO(env=env,
policy=policy,
value_function=value_function,
max_episode_length=max_episode_length,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
runner.setup(algo, env, sampler_cls=LocalSampler)
runner.train(n_epochs=2, batch_size=rollouts_per_task * max_episode_length)
def test_maml_trpo_dummy_named_env():
"""Test with dummy environment that has env_name."""
env = normalize(GymEnv(DummyMultiTaskBoxEnv(), max_episode_length=100),
expected_action_scale=10.)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32))
task_sampler = SetTaskSampler(
DummyMultiTaskBoxEnv,
wrapper=lambda env, _: normalize(GymEnv(env, max_episode_length=100),
expected_action_scale=10.))
episodes_per_task = 2
max_episode_length = env.spec.max_episode_length
sampler = LocalSampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length)
trainer = Trainer(snapshot_config)
algo = MAMLTRPO(env=env,
policy=policy,
sampler=sampler,
task_sampler=task_sampler,
value_function=value_function,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
trainer.setup(algo, env)
trainer.train(n_epochs=2,
batch_size=episodes_per_task * max_episode_length)