Model-Agnostic Meta-Learning (MAML)¶
Paper |
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks [1] |
Framework(s) |
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API Reference |
|
Code |
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Examples |
maml_ppo_half_cheetah_dir, maml_trpo_half_cheetah_dir, maml_trpo_metaworld_ml1_push, maml_trpo_metaworld_ml10. maml_trpo_metaworld_ml45 |
MAML is a meta-learning algorithm that trains the parameters of a policy such that they generalize well to unseen tasks. In essence, this technique produces models that are good few shot learners and easy to fine-tune.
Default Parameters¶
meta_batch_size=40,
inner_lr=0.1,
outer_lr=1e-3,
num_grad_updates=1,
meta_evaluator=None,
evaluate_every_n_epochs=1
Examples¶
maml_ppo_half_cheetah_dir¶
#!/usr/bin/env python3
"""This is an example to train MAML-VPG on HalfCheetahDirEnv environment."""
# pylint: disable=no-value-for-parameter
import click
import torch
from garage import wrap_experiment
from garage.envs import GymEnv, normalize
from garage.envs.mujoco import HalfCheetahDirEnv
from garage.experiment import MetaEvaluator
from garage.experiment.deterministic import set_seed
from garage.experiment.task_sampler import SetTaskSampler
from garage.torch.algos import MAMLPPO
from garage.torch.policies import GaussianMLPPolicy
from garage.torch.value_functions import GaussianMLPValueFunction
from garage.trainer import Trainer
@click.command()
@click.option('--seed', default=1)
@click.option('--epochs', default=300)
@click.option('--episodes_per_task', default=40)
@click.option('--meta_batch_size', default=20)
@wrap_experiment(snapshot_mode='all')
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)
maml_ppo_half_cheetah_dir()
maml_trpo_half_cheetah_dir¶
#!/usr/bin/env python3
"""This is an example to train MAML-VPG on HalfCheetahDirEnv environment."""
# pylint: disable=no-value-for-parameter
import click
import torch
from garage import wrap_experiment
from garage.envs import GymEnv, normalize
from garage.envs.mujoco import HalfCheetahDirEnv
from garage.experiment import MetaEvaluator
from garage.experiment.deterministic import set_seed
from garage.experiment.task_sampler import SetTaskSampler
from garage.torch.algos import MAMLTRPO
from garage.torch.policies import GaussianMLPPolicy
from garage.torch.value_functions import GaussianMLPValueFunction
from garage.trainer import Trainer
@click.command()
@click.option('--seed', default=1)
@click.option('--epochs', default=300)
@click.option('--episodes_per_task', default=40)
@click.option('--meta_batch_size', default=20)
@wrap_experiment(snapshot_mode='all')
def maml_trpo_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=1,
n_test_episodes=10)
trainer = Trainer(ctxt)
algo = MAMLTRPO(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)
maml_trpo_half_cheetah_dir()
maml_trpo_metaworld_ml1_push¶
#!/usr/bin/env python3
"""This is an example to train MAML-TRPO on ML1 Push environment."""
# pylint: disable=no-value-for-parameter
# yapf: disable
import click
import metaworld
import torch
from garage import wrap_experiment
from garage.envs import MetaWorldSetTaskEnv
from garage.experiment import (MetaEvaluator, MetaWorldTaskSampler,
SetTaskSampler)
from garage.experiment.deterministic import set_seed
from garage.torch.algos import MAMLTRPO
from garage.torch.policies import GaussianMLPPolicy
from garage.torch.value_functions import GaussianMLPValueFunction
from garage.trainer import Trainer
# yapf: enable
@click.command()
@click.option('--seed', default=1)
@click.option('--epochs', default=300)
@click.option('--rollouts_per_task', default=10)
@click.option('--meta_batch_size', default=20)
@wrap_experiment(snapshot_mode='all')
def maml_trpo_metaworld_ml1_push(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 Trainer 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)
ml1 = metaworld.ML1('push-v1')
tasks = MetaWorldTaskSampler(ml1, 'train')
env = tasks.sample(1)[0]()
test_sampler = SetTaskSampler(MetaWorldSetTaskEnv,
env=MetaWorldSetTaskEnv(ml1, 'test'))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(100, 100),
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)
meta_evaluator = MetaEvaluator(test_task_sampler=test_sampler,
n_test_tasks=1,
n_exploration_eps=rollouts_per_task)
trainer = Trainer(ctxt)
algo = MAMLTRPO(env=env,
policy=policy,
task_sampler=tasks,
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, n_workers=meta_batch_size)
trainer.train(n_epochs=epochs,
batch_size=rollouts_per_task * env.spec.max_episode_length)
maml_trpo_metaworld_ml1_push()
maml_trpo_metaworld_ml10¶
#!/usr/bin/env python3
"""This is an example to train MAML-TRPO on ML10 environment."""
# pylint: disable=no-value-for-parameter
# yapf: disable
import click
import metaworld
import torch
from garage import wrap_experiment
from garage.envs import MetaWorldSetTaskEnv
from garage.experiment import (MetaEvaluator, MetaWorldTaskSampler,
SetTaskSampler)
from garage.experiment.deterministic import set_seed
from garage.torch.algos import MAMLTRPO
from garage.torch.policies import GaussianMLPPolicy
from garage.torch.value_functions import GaussianMLPValueFunction
from garage.trainer import Trainer
# yapf: enable
@click.command()
@click.option('--seed', default=1)
@click.option('--epochs', default=300)
@click.option('--episodes_per_task', default=10)
@click.option('--meta_batch_size', default=20)
@wrap_experiment(snapshot_mode='all')
def maml_trpo_metaworld_ml10(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)
ml10 = metaworld.ML10()
tasks = MetaWorldTaskSampler(ml10, 'train')
env = tasks.sample(10)[0]()
test_sampler = SetTaskSampler(MetaWorldSetTaskEnv,
env=MetaWorldSetTaskEnv(ml10, 'test'))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(100, 100),
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)
meta_evaluator = MetaEvaluator(test_task_sampler=test_sampler)
trainer = Trainer(ctxt)
algo = MAMLTRPO(env=env,
policy=policy,
task_sampler=tasks,
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, n_workers=meta_batch_size)
trainer.train(n_epochs=epochs,
batch_size=episodes_per_task * env.spec.max_episode_length)
maml_trpo_metaworld_ml10()
maml_trpo_metaworld_ml45¶
#!/usr/bin/env python3
"""This is an example to train MAML-TRPO on ML45 environment."""
# pylint: disable=no-value-for-parameter
# yapf: disable
import click
import metaworld
import torch
from garage import wrap_experiment
from garage.envs import MetaWorldSetTaskEnv, normalize
from garage.experiment import (MetaEvaluator, MetaWorldTaskSampler,
SetTaskSampler)
from garage.experiment.deterministic import set_seed
from garage.torch.algos import MAMLTRPO
from garage.torch.policies import GaussianMLPPolicy
from garage.torch.value_functions import GaussianMLPValueFunction
from garage.trainer import Trainer
# yapf: enable
@click.command()
@click.option('--seed', default=1)
@click.option('--epochs', default=300)
@click.option('--episodes_per_task', default=45)
@click.option('--meta_batch_size', default=45)
@wrap_experiment(snapshot_mode='all')
def maml_trpo_metaworld_ml45(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)
ml45 = metaworld.ML45()
# pylint: disable=missing-return-doc,missing-return-type-doc
def wrap(env, _):
return normalize(env, expected_action_scale=10.0)
train_task_sampler = MetaWorldTaskSampler(ml45, 'train', wrap)
test_env = wrap(MetaWorldSetTaskEnv(ml45, 'test'), None)
test_task_sampler = SetTaskSampler(MetaWorldSetTaskEnv,
env=test_env,
wrapper=wrap)
env = train_task_sampler.sample(45)[0]()
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(100, 100),
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)
meta_evaluator = MetaEvaluator(test_task_sampler=test_task_sampler)
trainer = Trainer(ctxt)
algo = MAMLTRPO(env=env,
task_sampler=train_task_sampler,
policy=policy,
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, n_workers=meta_batch_size)
trainer.train(n_epochs=epochs,
batch_size=episodes_per_task * env.spec.max_episode_length)
maml_trpo_metaworld_ml45()
References¶
- 1
Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. 2017. arXiv:1703.03400.
This page was authored by Mishari Aliesa (@maliesa96).