import dataclasses
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import ActionSpace
from ...models.builders import create_discrete_q_function
from ...models.encoders import EncoderFactory, make_encoder_field
from ...models.optimizers import OptimizerFactory, make_optimizer_field
from ...models.q_functions import QFunctionFactory, make_q_func_field
from ...types import Shape
from .base import QLearningAlgoBase
from .torch.dqn_impl import DQNImpl, DQNModules
__all__ = ["NFQConfig", "NFQ"]
[docs]@dataclasses.dataclass()
class NFQConfig(LearnableConfig):
r"""Config of Neural Fitted Q Iteration algorithm.
This NFQ implementation in d3rlpy is practically same as DQN, but excluding
the target network mechanism.
.. math::
L(\theta) = \mathbb{E}_{s_t, a_t, r_{t+1}, s_{t+1} \sim D} [(r_{t+1}
+ \gamma \max_a Q_{\theta'}(s_{t+1}, a) - Q_\theta(s_t, a_t))^2]
where :math:`\theta'` is the target network parameter. The target network
parameter is synchronized every `target_update_interval` iterations.
References:
* `Riedmiller., Neural Fitted Q Iteration - first experiences with a
data efficient neural reinforcement learning method.
<https://link.springer.com/chapter/10.1007/11564096_32>`_
Args:
observation_scaler (d3rlpy.preprocessing.ObservationScaler):
Observation preprocessor.
reward_scaler (d3rlpy.preprocessing.RewardScaler): Reward preprocessor.
learning_rate (float): Learning rate.
optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
Optimizer factory.
encoder_factory (d3rlpy.models.encoders.EncoderFactory):
Encoder factory.
q_func_factory (d3rlpy.models.q_functions.QFunctionFactory):
Q function factory.
batch_size (int): Mini-batch size.
gamma (float): Discount factor.
n_critics (int): Number of Q functions for ensemble.
"""
learning_rate: float = 6.25e-5
optim_factory: OptimizerFactory = make_optimizer_field()
encoder_factory: EncoderFactory = make_encoder_field()
q_func_factory: QFunctionFactory = make_q_func_field()
batch_size: int = 32
gamma: float = 0.99
n_critics: int = 1
[docs] def create(self, device: DeviceArg = False) -> "NFQ":
return NFQ(self, device)
@staticmethod
def get_type() -> str:
return "nfq"
[docs]class NFQ(QLearningAlgoBase[DQNImpl, NFQConfig]):
def inner_create_impl(
self, observation_shape: Shape, action_size: int
) -> None:
q_funcs, q_func_forwarder = create_discrete_q_function(
observation_shape,
action_size,
self._config.encoder_factory,
self._config.q_func_factory,
n_ensembles=self._config.n_critics,
device=self._device,
)
targ_q_funcs, targ_q_func_forwarder = create_discrete_q_function(
observation_shape,
action_size,
self._config.encoder_factory,
self._config.q_func_factory,
n_ensembles=self._config.n_critics,
device=self._device,
)
optim = self._config.optim_factory.create(
q_funcs.named_modules(), lr=self._config.learning_rate
)
modules = DQNModules(
q_funcs=q_funcs,
targ_q_funcs=targ_q_funcs,
optim=optim,
)
self._impl = DQNImpl(
observation_shape=observation_shape,
action_size=action_size,
modules=modules,
q_func_forwarder=q_func_forwarder,
targ_q_func_forwarder=targ_q_func_forwarder,
target_update_interval=1,
gamma=self._config.gamma,
device=self._device,
)
[docs] def get_action_type(self) -> ActionSpace:
return ActionSpace.DISCRETE
register_learnable(NFQConfig)