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 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:`~LocalRunner` 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)
env = normalize(GymEnv(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_episode_length = 100
task_sampler = SetTaskSampler(lambda: normalize(
GymEnv(HalfCheetahDirEnv()), expected_action_scale=10.))
meta_evaluator = MetaEvaluator(test_task_sampler=task_sampler,
max_episode_length=max_episode_length,
n_test_tasks=1,
n_test_episodes=10)
runner = LocalRunner(ctxt)
algo = MAMLPPO(env=env,
policy=policy,
value_function=value_function,
max_episode_length=max_episode_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=episodes_per_task * max_episode_length)
def maml_trpo(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)
# @TODO blowing up here...
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 = MAMLTRPO(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):
super().setup_method()
self.max_episode_length = 100
self.meta_batch_size = 10
self.episode_per_task = 4
self.tasks = task_sampler.SetTaskSampler(
lambda: RL2Env(env=normalize(HalfCheetahDirEnv())))
self.env_spec = RL2Env(env=normalize(HalfCheetahDirEnv())).spec
self.policy = GaussianGRUPolicy(env_spec=self.env_spec,
hidden_dim=64,
state_include_action=False)
self.baseline = LinearFeatureBaseline(env_spec=self.env_spec)
def test_maml_trpo_pendulum():
"""Test PPO with Pendulum environment."""
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))
rollouts_per_task = 5
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)
last_avg_ret = runner.train(n_epochs=5,
batch_size=rollouts_per_task *
max_episode_length)
assert last_avg_ret > -5
env.close()
def test_ppo_pendulum(self):
"""Test PPO with Pendulum environment."""
deterministic.set_seed(0)
rollouts_per_task = 5
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(snapshot_config)
algo = MAMLVPG(env=self.env,
policy=self.policy,
value_function=self.value_function,
max_path_length=max_path_length,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1,
meta_evaluator=meta_evaluator)
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
def setup_method(self):
super().setup_method()
self.meta_batch_size = 10
self.episode_per_task = 4
self.max_episode_length = 100
# Avoid pickling self
max_episode_length = 100
self.tasks = task_sampler.SetTaskSampler(
HalfCheetahDirEnv,
wrapper=lambda env, _: RL2Env(
normalize(GymEnv(env, max_episode_length=max_episode_length))))
self.env_spec = RL2Env(
normalize(
GymEnv(HalfCheetahDirEnv(),
max_episode_length=max_episode_length))).spec
self.policy = GaussianGRUPolicy(env_spec=self.env_spec,
hidden_dim=64,
state_include_action=False)
self.baseline = LinearFeatureBaseline(env_spec=self.env_spec)
self.sampler = LocalSampler(
agents=self.policy,
envs=self.tasks.sample(self.meta_batch_size),
max_episode_length=self.env_spec.max_episode_length,
is_tf_worker=True,
n_workers=self.meta_batch_size,
worker_class=RL2Worker)
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)
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))
def test_maml_trpo_pendulum():
"""Test PPO with Pendulum environment."""
episodes_per_task = 5
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))
task_sampler = SetTaskSampler(
HalfCheetahDirEnv,
wrapper=lambda env, _: normalize(GymEnv(
env, max_episode_length=max_episode_length),
expected_action_scale=10.))
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)
last_avg_ret = trainer.train(n_epochs=5,
batch_size=episodes_per_task *
max_episode_length)
assert last_avg_ret > -5
env.close()
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.)
self.task_sampler = SetTaskSampler(
HalfCheetahDirEnv,
wrapper=lambda env, _: normalize(GymEnv(env,
max_episode_length=100),
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))
def maml_trpo(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,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
max_path_length = 100
runner = LocalRunner(ctxt)
algo = MAMLTRPO(env=env,
policy=policy,
baseline=baseline,
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)
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 = 20
max_path_length = 100
runner = LocalRunner(snapshot_config)
algo = MAMLVPG(env=env,
policy=policy,
baseline=baseline,
max_path_length=max_path_length,
meta_batch_size=40,
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)
