import dataclasses
import math
from ...base import DeviceArg, register_learnable
from ...models.builders import (
create_continuous_q_function,
create_normal_policy,
create_parameter,
)
from ...types import Shape
from .cql import CQL, CQLConfig
from .torch.cal_ql_impl import CalQLImpl
from .torch.cql_impl import CQLModules
__all__ = ["CalQLConfig", "CalQL"]
[docs]@dataclasses.dataclass()
class CalQLConfig(CQLConfig):
r"""Config of Calibrated Q-Learning algorithm.
Cal-QL is an extension to CQL to mitigate issues in offline-to-online
fine-tuning.
The CQL regularizer is modified as follows:
.. math::
\mathbb{E}_{s \sim D, a \sim \pi} [\max{(Q(s, a), V(s))}]
- \mathbb{E}_{s, a \sim D} [Q(s, a)]
References:
* `Mitsuhiko et al., Cal-QL: Calibrated Offline RL Pre-Training for
Efficient Online Fine-Tuning. <https://arxiv.org/abs/2303.05479>`_
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 policy function.
critic_learning_rate (float): Learning rate for Q functions.
temp_learning_rate (float):
Learning rate for temperature parameter of SAC.
alpha_learning_rate (float): Learning rate for :math:`\alpha`.
actor_optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
Optimizer factory for the actor.
critic_optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
Optimizer factory for the critic.
temp_optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
Optimizer factory for the temperature.
alpha_optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
Optimizer factory for :math:`\alpha`.
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.
initial_temperature (float): Initial temperature value.
initial_alpha (float): Initial :math:`\alpha` value.
alpha_threshold (float): Threshold value described as :math:`\tau`.
conservative_weight (float): Constant weight to scale conservative loss.
n_action_samples (int): Number of sampled actions to compute
:math:`\log{\sum_a \exp{Q(s, a)}}`.
soft_q_backup (bool): Flag to use SAC-style backup.
"""
[docs] def create(self, device: DeviceArg = False) -> "CalQL":
return CalQL(self, device)
@staticmethod
def get_type() -> str:
return "cal_ql"
[docs]class CalQL(CQL):
def inner_create_impl(
self, observation_shape: Shape, action_size: int
) -> None:
policy = create_normal_policy(
observation_shape,
action_size,
self._config.actor_encoder_factory,
device=self._device,
)
q_funcs, q_func_fowarder = 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,
)
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,
)
log_temp = create_parameter(
(1, 1),
math.log(self._config.initial_temperature),
device=self._device,
)
log_alpha = create_parameter(
(1, 1), math.log(self._config.initial_alpha), device=self._device
)
actor_optim = self._config.actor_optim_factory.create(
policy.named_modules(), lr=self._config.actor_learning_rate
)
critic_optim = self._config.critic_optim_factory.create(
q_funcs.named_modules(), lr=self._config.critic_learning_rate
)
if self._config.temp_learning_rate > 0:
temp_optim = self._config.temp_optim_factory.create(
log_temp.named_modules(), lr=self._config.temp_learning_rate
)
else:
temp_optim = None
if self._config.alpha_learning_rate > 0:
alpha_optim = self._config.alpha_optim_factory.create(
log_alpha.named_modules(), lr=self._config.alpha_learning_rate
)
else:
alpha_optim = None
modules = CQLModules(
policy=policy,
q_funcs=q_funcs,
targ_q_funcs=targ_q_funcs,
log_temp=log_temp,
log_alpha=log_alpha,
actor_optim=actor_optim,
critic_optim=critic_optim,
temp_optim=temp_optim,
alpha_optim=alpha_optim,
)
self._impl = CalQLImpl(
observation_shape=observation_shape,
action_size=action_size,
modules=modules,
q_func_forwarder=q_func_fowarder,
targ_q_func_forwarder=targ_q_func_forwarder,
gamma=self._config.gamma,
tau=self._config.tau,
alpha_threshold=self._config.alpha_threshold,
conservative_weight=self._config.conservative_weight,
n_action_samples=self._config.n_action_samples,
soft_q_backup=self._config.soft_q_backup,
device=self._device,
)
@property
def need_returns_to_go(self) -> bool:
return True
register_learnable(CalQLConfig)