from .base import AlgoBase
from .torch.awac_impl import AWACImpl
from ..optimizers import AdamFactory
from ..argument_utils import check_encoder
from ..argument_utils import check_use_gpu
from ..argument_utils import check_q_func
from ..argument_utils import check_augmentation
[docs]class AWAC(AlgoBase):
r""" Advantage Weighted Actor-Critic algorithm.
AWAC is a TD3-based actor-critic algorithm that enables efficient
fine-tuning where the policy is trained with offline datasets and is
deployed to online training.
The policy is trained as a supervised regression.
.. math::
J(\phi) = \mathbb{E}_{s_t, a_t \sim D}
[\log \pi_\phi(a_t|s_t)
\exp(\frac{1}{\lambda} A^\pi (s_t, a_t))]
where :math:`A^\pi (s_t, a_t) = Q_\theta(s_t, a_t) -
Q_\theta(s_t, a'_t)` and :math:`a'_t \sim \pi_\phi(\cdot|s_t)`
The key difference from AWR is that AWAC uses Q-function trained via TD
learning for the better sample-efficiency.
References:
* `Nair et al., Accelerating Online Reinforcement Learning with Offline
Datasets. <https://arxiv.org/abs/2006.09359>`_
Args:
actor_learning_rate (float): learning rate for policy function.
critic_learning_rate (float): learning rate for Q functions.
actor_optim_factory (d3rlpy.optimizers.OptimizerFactory):
optimizer factory for the actor.
critic_optim_factory (d3rlpy.optimizers.OptimizerFactory):
optimizer factory for the critic.
actor_encoder_factory (d3rlpy.encoders.EncoderFactory or str):
encoder factory for the actor.
critic_encoder_factory (d3rlpy.encoders.EncoderFactory or str):
encoder factory for the critic.
q_func_factory (d3rlpy.q_functions.QFunctionFactory or str):
Q function factory.
batch_size (int): mini-batch size.
n_frames (int): the number of frames to stack for image observation.
n_steps (int): N-step TD calculation.
gamma (float): discount factor.
tau (float): target network synchronization coefficiency.
lam (float): :math:`\lambda` for weight calculation.
n_action_samples (int): the number of sampled actions to calculate
:math:`A^\pi(s_t, a_t)`.
max_weight (float): maximum weight for cross-entropy loss.
n_critics (int): the number of Q functions for ensemble.
bootstrap (bool): flag to bootstrap Q functions.
share_encoder (bool): flag to share encoder network.
update_actor_interval (int): interval to update policy function.
use_gpu (bool, int or d3rlpy.gpu.Device):
flag to use GPU, device ID or device.
scaler (d3rlpy.preprocessing.Scaler or str): preprocessor.
The available options are `['pixel', 'min_max', 'standard']`
augmentation (d3rlpy.augmentation.AugmentationPipeline or list(str)):
augmentation pipeline.
dynamics (d3rlpy.dynamics.base.DynamicsBase): dynamics model for data
augmentation.
impl (d3rlpy.algos.torch.sac_impl.SACImpl): algorithm implementation.
Attributes:
actor_learning_rate (float): learning rate for policy function.
critic_learning_rate (float): learning rate for Q functions.
actor_optim_factory (d3rlpy.optimizers.OptimizerFactory):
optimizer factory for the actor.
critic_optim_factory (d3rlpy.optimizers.OptimizerFactory):
optimizer factory for the critic.
actor_encoder_factory (d3rlpy.encoders.EncoderFactory):
encoder factory for the actor.
critic_encoder_factory (d3rlpy.encoders.EncoderFactory):
encoder factory for the critic.
q_func_factory (d3rlpy.q_functions.QFunctionFactory):
Q function factory.
batch_size (int): mini-batch size.
n_frames (int): the number of frames to stack for image observation.
n_steps (int): N-step TD calculation.
gamma (float): discount factor.
tau (float): target network synchronization coefficiency.
lam (float): :math:`\lambda` for weight calculation.
n_action_samples (int): the number of sampled actions to calculate
:math:`A^\pi(s_t, a_t)`.
max_weight (float): maximum weight for cross-entropy loss.
n_critics (int): the number of Q functions for ensemble.
bootstrap (bool): flag to bootstrap Q functions.
share_encoder (bool): flag to share encoder network.
update_actor_interval (int): interval to update policy function.
use_gpu (bool, int or d3rlpy.gpu.Device):
flag to use GPU, device ID or device.
scaler (d3rlpy.preprocessing.Scaler or str): preprocessor.
augmentation (d3rlpy.augmentation.AugmentationPipeline or list(str)):
augmentation pipeline.
dynamics (d3rlpy.dynamics.base.DynamicsBase): dynamics model for data
augmentation.
impl (d3rlpy.algos.torch.sac_impl.SACImpl): algorithm implementation.
"""
def __init__(self,
*,
actor_learning_rate=3e-4,
critic_learning_rate=3e-4,
actor_optim_factory=AdamFactory(weight_decay=1e-4),
critic_optim_factory=AdamFactory(),
actor_encoder_factory='default',
critic_encoder_factory='default',
q_func_factory='mean',
batch_size=1024,
n_frames=1,
n_steps=1,
gamma=0.99,
tau=0.005,
lam=1.0,
n_action_samples=1,
max_weight=20.0,
n_critics=2,
bootstrap=False,
share_encoder=False,
update_actor_interval=1,
use_gpu=False,
scaler=None,
augmentation=None,
dynamics=None,
impl=None,
**kwargs):
super().__init__(batch_size=batch_size,
n_frames=n_frames,
n_steps=n_steps,
gamma=gamma,
scaler=scaler,
dynamics=dynamics)
self.actor_learning_rate = actor_learning_rate
self.critic_learning_rate = critic_learning_rate
self.actor_optim_factory = actor_optim_factory
self.critic_optim_factory = critic_optim_factory
self.actor_encoder_factory = check_encoder(actor_encoder_factory)
self.critic_encoder_factory = check_encoder(critic_encoder_factory)
self.q_func_factory = check_q_func(q_func_factory)
self.tau = tau
self.lam = lam
self.n_action_samples = n_action_samples
self.max_weight = max_weight
self.n_critics = n_critics
self.bootstrap = bootstrap
self.share_encoder = share_encoder
self.update_actor_interval = update_actor_interval
self.augmentation = check_augmentation(augmentation)
self.use_gpu = check_use_gpu(use_gpu)
self.impl = impl
[docs] def create_impl(self, observation_shape, action_size):
self.impl = AWACImpl(
observation_shape=observation_shape,
action_size=action_size,
actor_learning_rate=self.actor_learning_rate,
critic_learning_rate=self.critic_learning_rate,
actor_optim_factory=self.actor_optim_factory,
critic_optim_factory=self.critic_optim_factory,
actor_encoder_factory=self.actor_encoder_factory,
critic_encoder_factory=self.critic_encoder_factory,
q_func_factory=self.q_func_factory,
gamma=self.gamma,
tau=self.tau,
lam=self.lam,
n_action_samples=self.n_action_samples,
max_weight=self.max_weight,
n_critics=self.n_critics,
bootstrap=self.bootstrap,
share_encoder=self.share_encoder,
use_gpu=self.use_gpu,
scaler=self.scaler,
augmentation=self.augmentation)
self.impl.build()
[docs] def update(self, epoch, total_step, batch):
critic_loss = self.impl.update_critic(batch.observations,
batch.actions,
batch.next_rewards,
batch.next_observations,
batch.terminals, batch.n_steps)
# delayed policy update
if total_step % self.update_actor_interval == 0:
actor_loss, mean_std = self.impl.update_actor(
batch.observations, batch.actions)
self.impl.update_critic_target()
self.impl.update_actor_target()
else:
actor_loss, mean_std = None, None
return critic_loss, actor_loss, mean_std
def _get_loss_labels(self):
return ['critic_loss', 'actor_loss', 'mean_std']