def setup_method(self):
"""Setup method which is called before every test."""
self.env = normalize(GymEnv(HalfCheetahDirEnv(),
max_episode_length=100),
expected_action_scale=10.)
task_sampler = SetTaskSampler(lambda: normalize(
GymEnv(HalfCheetahDirEnv()), expected_action_scale=10.))
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.value_function = GaussianMLPValueFunction(env_spec=self.env.spec,
hidden_sizes=(32, 32))
self.algo = MAMLPPO(env=self.env,
policy=self.policy,
sampler=None,
task_sampler=task_sampler,
value_function=self.value_function,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
def test_ppo_pendulum(self):
"""Test PPO with Pendulum environment."""
deterministic.set_seed(0)
episodes_per_task = 5
max_episode_length = self.env.spec.max_episode_length
trainer = Trainer(snapshot_config)
algo = MAMLPPO(env=self.env,
policy=self.policy,
sampler=self.sampler,
task_sampler=self.task_sampler,
value_function=self.value_function,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
trainer.setup(algo, self.env)
last_avg_ret = trainer.train(n_epochs=10,
batch_size=episodes_per_task *
max_episode_length)
assert last_avg_ret > -5
def load_mamlppo(env_name="MountainCarContinuous-v0"):
"""Return an instance of the MAML-PPO algorithm."""
env = GarageEnv(env_name=env_name)
policy = DeterministicMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=[64, 64])
vfunc = GaussianMLPValueFunction(env_spec=env.spec)
task_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(env, expected_action_scale=10.)))
max_path_length = 100
meta_evaluator = MetaEvaluator(test_task_sampler=task_sampler,
max_path_length=max_path_length,
n_test_tasks=1,
n_test_rollouts=10)
algo = MAMLPPO(env=env,
policy=policy,
value_function=vfunc,
max_path_length=max_path_length,
meta_batch_size=20,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1,
meta_evaluator=meta_evaluator)
return algo
def maml_ppo_half_cheetah_dir(ctxt, seed, epochs, episodes_per_task,
meta_batch_size):
"""Set up environment and algorithm and run the task.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
seed (int): Used to seed the random number generator to produce
determinism.
epochs (int): Number of training epochs.
episodes_per_task (int): Number of episodes per epoch per task
for training.
meta_batch_size (int): Number of tasks sampled per batch.
"""
set_seed(seed)
max_episode_length = 100
env = normalize(GymEnv(HalfCheetahDirEnv(),
max_episode_length=max_episode_length),
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),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
task_sampler = SetTaskSampler(
HalfCheetahDirEnv,
wrapper=lambda env, _: normalize(GymEnv(
env, max_episode_length=max_episode_length),
expected_action_scale=10.))
meta_evaluator = MetaEvaluator(test_task_sampler=task_sampler,
n_test_tasks=2,
n_test_episodes=10)
trainer = Trainer(ctxt)
algo = MAMLPPO(env=env,
policy=policy,
task_sampler=task_sampler,
value_function=value_function,
meta_batch_size=meta_batch_size,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1,
meta_evaluator=meta_evaluator)
trainer.setup(algo, env)
trainer.train(n_epochs=epochs,
batch_size=episodes_per_task * env.spec.max_episode_length)
def maml_ppo_half_cheetah_dir(ctxt, seed, epochs, rollouts_per_task,
meta_batch_size):
"""Set up environment and algorithm and run the task.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
epochs (int): Number of training epochs.
rollouts_per_task (int): Number of rollouts per epoch per task
for training.
meta_batch_size (int): Number of tasks sampled per batch.
"""
set_seed(seed)
env = GarageEnv(normalize(HalfCheetahDirEnv(), 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),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
max_path_length = 100
task_sampler = SetTaskSampler(lambda: GarageEnv(
normalize(HalfCheetahDirEnv(), expected_action_scale=10.)))
meta_evaluator = MetaEvaluator(test_task_sampler=task_sampler,
max_path_length=max_path_length,
n_test_tasks=1,
n_test_rollouts=10)
runner = LocalRunner(ctxt)
algo = MAMLPPO(env=env,
policy=policy,
value_function=value_function,
max_path_length=max_path_length,
meta_batch_size=meta_batch_size,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1,
meta_evaluator=meta_evaluator)
runner.setup(algo, env)
runner.train(n_epochs=epochs,
batch_size=rollouts_per_task * max_path_length)
def setup_method(self):
"""Setup method which is called before every test."""
self.env = GarageEnv(
normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.value_function = LinearFeatureBaseline(env_spec=self.env.spec)
self.algo = MAMLPPO(env=self.env,
policy=self.policy,
value_function=self.value_function,
max_path_length=100,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
def run_task(snapshot_config, *_):
"""Set up environment and algorithm and run the task.
Args:
snapshot_config (garage.experiment.SnapshotConfig): The snapshot
configuration used by LocalRunner to create the snapshotter.
If None, it will create one with default settings.
_ : Unused parameters
"""
env = GarageEnv(normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
rollouts_per_task = 40
max_path_length = 100
runner = LocalRunner(snapshot_config)
algo = MAMLPPO(env=env,
policy=policy,
baseline=baseline,
max_path_length=max_path_length,
meta_batch_size=20,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
runner.setup(algo, env)
runner.train(n_epochs=300, batch_size=rollouts_per_task * max_path_length)
def test_ppo_pendulum(self):
"""Test PPO with Pendulum environment."""
deterministic.set_seed(0)
rollouts_per_task = 5
max_path_length = 100
runner = LocalRunner(snapshot_config)
algo = MAMLPPO(env=self.env,
policy=self.policy,
baseline=self.baseline,
max_path_length=max_path_length,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
runner.setup(algo, self.env)
last_avg_ret = runner.train(n_epochs=10,
batch_size=rollouts_per_task *
max_path_length)
assert last_avg_ret > -5
class TestMAML:
"""Test class for MAML."""
def setup_method(self):
"""Setup method which is called before every test."""
self.env = normalize(GymEnv(HalfCheetahDirEnv()),
expected_action_scale=10.)
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.value_function = GaussianMLPValueFunction(env_spec=self.env.spec,
hidden_sizes=(32, 32))
self.algo = MAMLPPO(env=self.env,
policy=self.policy,
value_function=self.value_function,
max_episode_length=100,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
def teardown_method(self):
"""Teardown method which is called after every test."""
self.env.close()
@staticmethod
def _set_params(v, m):
"""Set the parameters of a module to a value."""
if isinstance(m, torch.nn.Linear):
m.weight.data.fill_(v)
m.bias.data.fill_(v)
@staticmethod
def _test_params(v, m):
"""Test if all parameters of a module equal to a value."""
if isinstance(m, torch.nn.Linear):
assert torch.all(torch.eq(m.weight.data, v))
assert torch.all(torch.eq(m.bias.data, v))
def test_get_exploration_policy(self):
"""Test if an independent copy of policy is returned."""
self.policy.apply(partial(self._set_params, 0.1))
adapt_policy = self.algo.get_exploration_policy()
adapt_policy.apply(partial(self._set_params, 0.2))
# Old policy should remain untouched
self.policy.apply(partial(self._test_params, 0.1))
adapt_policy.apply(partial(self._test_params, 0.2))
def test_adapt_policy(self):
"""Test if policy can adapt to samples."""
worker = WorkerFactory(seed=100, max_episode_length=100)
sampler = LocalSampler.from_worker_factory(worker, self.policy,
self.env)
self.policy.apply(partial(self._set_params, 0.1))
adapt_policy = self.algo.get_exploration_policy()
eps = sampler.obtain_samples(0, 100, adapt_policy)
self.algo.adapt_policy(adapt_policy, eps)
# Old policy should remain untouched
self.policy.apply(partial(self._test_params, 0.1))
# Adapted policy should not be identical to old policy
for v1, v2 in zip(adapt_policy.parameters(), self.policy.parameters()):
if v1.data.ne(v2.data).sum() > 0:
break
else:
pytest.fail('Parameters of adapted policy should not be '
'identical to the old policy.')
