import dataclasses
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import ActionSpace
from ...models.builders import (
create_continuous_q_function,
create_deterministic_policy,
)
from ...models.encoders import EncoderFactory, make_encoder_field
from ...models.q_functions import QFunctionFactory, make_q_func_field
from ...optimizers.optimizers import OptimizerFactory, make_optimizer_field
from ...types import Shape
from .base import QLearningAlgoBase
from .torch.ddpg_impl import DDPGModules
from .torch.rebrac_impl import ReBRACImpl
__all__ = ["ReBRACConfig", "ReBRAC"]
[docs]@dataclasses.dataclass()
class ReBRACConfig(LearnableConfig):
r"""Config of ReBRAC algorithm.
ReBRAC is an extention to TD3+BC with additional optimization.
#. Deeper Networks (2 -> 3 hidden layers)
#. LayerNorm
#. Larger Batches (256 -> 1024)
#. Increased Discount Factor (0.99 -> 0.999)
#. Actor and Critic penalty decoupling
.. math::
J(\phi) = \mathbb{E}_{s,a \sim D}
[\lambda Q(s, \pi(s)) - \beta_1 \cdot (a - \pi(s))^2]
.. math::
L(\theta) = \mathbb{E}_{s,a,r,s',\hat{a'} \sim D, a' \sim \pi(s')}
[(Q_\theta (s, a) - (r + \gamma Q_\theta (s', a')
- \beta_2 \cdot (a' - \hat{a'})^2))^2]
References:
* `Tarasov et al., Revisiting the Minimalist Approach to Offline
Reinforcement Learning. <https://arxiv.org/abs/2305.09836>`_
Args:
observation_scaler (d3rlpy.preprocessing.ObservationScaler):
Observation preprocessor.
action_scaler (d3rlpy.preprocessing.ActionScaler): Action preprocessor.
reward_scaler (d3rlpy.preprocessing.RewardScaler): Reward preprocessor.
actor_learning_rate (float): Learning rate for a 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.models.encoders.EncoderFactory):
Encoder factory for the actor.
critic_encoder_factory (d3rlpy.models.encoders.EncoderFactory):
Encoder factory for the critic.
q_func_factory (d3rlpy.models.q_functions.QFunctionFactory):
Q function factory.
batch_size (int): Mini-batch size.
gamma (float): Discount factor.
tau (float): Target network synchronization coefficiency.
n_critics (int): Number of Q functions for ensemble.
target_smoothing_sigma (float): Standard deviation for target noise.
target_smoothing_clip (float): Clipping range for target noise.
actor_beta (float): :math:`\beta_1` value.
critic_beta (float): :math:`\beta_2` value.
update_actor_interval (int): Interval to update policy function
described as `delayed policy update` in the paper.
compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
"""
actor_learning_rate: float = 1e-3
critic_learning_rate: float = 1e-3
actor_optim_factory: OptimizerFactory = make_optimizer_field()
critic_optim_factory: OptimizerFactory = make_optimizer_field()
actor_encoder_factory: EncoderFactory = make_encoder_field()
critic_encoder_factory: EncoderFactory = make_encoder_field()
q_func_factory: QFunctionFactory = make_q_func_field()
batch_size: int = 1024
gamma: float = 0.99
tau: float = 0.005
n_critics: int = 2
target_smoothing_sigma: float = 0.2
target_smoothing_clip: float = 0.5
actor_beta: float = 0.001
critic_beta: float = 0.01
update_actor_interval: int = 2
[docs] def create(
self, device: DeviceArg = False, enable_ddp: bool = False
) -> "ReBRAC":
return ReBRAC(self, device, enable_ddp)
@staticmethod
def get_type() -> str:
return "rebrac"
[docs]class ReBRAC(QLearningAlgoBase[ReBRACImpl, ReBRACConfig]):
def inner_create_impl(
self, observation_shape: Shape, action_size: int
) -> None:
policy = create_deterministic_policy(
observation_shape,
action_size,
self._config.actor_encoder_factory,
device=self._device,
enable_ddp=self._enable_ddp,
)
targ_policy = create_deterministic_policy(
observation_shape,
action_size,
self._config.actor_encoder_factory,
device=self._device,
enable_ddp=self._enable_ddp,
)
q_funcs, q_func_forwarder = create_continuous_q_function(
observation_shape,
action_size,
self._config.critic_encoder_factory,
self._config.q_func_factory,
n_ensembles=self._config.n_critics,
device=self._device,
enable_ddp=self._enable_ddp,
)
targ_q_funcs, targ_q_func_forwarder = create_continuous_q_function(
observation_shape,
action_size,
self._config.critic_encoder_factory,
self._config.q_func_factory,
n_ensembles=self._config.n_critics,
device=self._device,
enable_ddp=self._enable_ddp,
)
actor_optim = self._config.actor_optim_factory.create(
policy.named_modules(),
lr=self._config.actor_learning_rate,
compiled=self.compiled,
)
critic_optim = self._config.critic_optim_factory.create(
q_funcs.named_modules(),
lr=self._config.critic_learning_rate,
compiled=self.compiled,
)
modules = DDPGModules(
policy=policy,
targ_policy=targ_policy,
q_funcs=q_funcs,
targ_q_funcs=targ_q_funcs,
actor_optim=actor_optim,
critic_optim=critic_optim,
)
self._impl = ReBRACImpl(
observation_shape=observation_shape,
action_size=action_size,
modules=modules,
q_func_forwarder=q_func_forwarder,
targ_q_func_forwarder=targ_q_func_forwarder,
gamma=self._config.gamma,
tau=self._config.tau,
target_smoothing_sigma=self._config.target_smoothing_sigma,
target_smoothing_clip=self._config.target_smoothing_clip,
actor_beta=self._config.actor_beta,
critic_beta=self._config.critic_beta,
update_actor_interval=self._config.update_actor_interval,
compiled=self.compiled,
device=self._device,
)
[docs] def get_action_type(self) -> ActionSpace:
return ActionSpace.CONTINUOUS
register_learnable(ReBRACConfig)