from typing import Any, Dict, Optional, Sequence
from ..argument_utility import (
EncoderArg,
QFuncArg,
RewardScalerArg,
ScalerArg,
UseGPUArg,
check_encoder,
check_q_func,
check_use_gpu,
)
from ..constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ..dataset import TransitionMiniBatch
from ..gpu import Device
from ..models.encoders import EncoderFactory
from ..models.optimizers import AdamFactory, OptimizerFactory
from ..models.q_functions import QFunctionFactory
from .base import AlgoBase
from .torch.dqn_impl import DoubleDQNImpl, DQNImpl
[docs]class DQN(AlgoBase):
r"""Deep Q-Network algorithm.
.. 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:
* `Mnih et al., Human-level control through deep reinforcement
learning. <https://www.nature.com/articles/nature14236>`_
Args:
learning_rate (float): learning rate.
optim_factory (d3rlpy.models.optimizers.OptimizerFactory or str):
optimizer factory.
encoder_factory (d3rlpy.models.encoders.EncoderFactory or str):
encoder factory.
q_func_factory (d3rlpy.models.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.
n_critics (int): the number of Q functions for ensemble.
target_reduction_type (str): ensemble reduction method at target value
estimation. The available options are
``['min', 'max', 'mean', 'mix', 'none']``.
target_update_interval (int): interval to update the target network.
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']`
reward_scaler (d3rlpy.preprocessing.RewardScaler or str):
reward preprocessor. The available options are
``['clip', 'min_max', 'standard']``.
impl (d3rlpy.algos.torch.dqn_impl.DQNImpl): algorithm implementation.
"""
_learning_rate: float
_optim_factory: OptimizerFactory
_encoder_factory: EncoderFactory
_q_func_factory: QFunctionFactory
_n_critics: int
_target_reduction_type: str
_target_update_interval: int
_use_gpu: Optional[Device]
_impl: Optional[DQNImpl]
def __init__(
self,
*,
learning_rate: float = 6.25e-5,
optim_factory: OptimizerFactory = AdamFactory(),
encoder_factory: EncoderArg = "default",
q_func_factory: QFuncArg = "mean",
batch_size: int = 32,
n_frames: int = 1,
n_steps: int = 1,
gamma: float = 0.99,
n_critics: int = 1,
target_reduction_type: str = "min",
target_update_interval: int = 8000,
use_gpu: UseGPUArg = False,
scaler: ScalerArg = None,
reward_scaler: RewardScalerArg = None,
impl: Optional[DQNImpl] = None,
**kwargs: Any,
):
super().__init__(
batch_size=batch_size,
n_frames=n_frames,
n_steps=n_steps,
gamma=gamma,
scaler=scaler,
reward_scaler=reward_scaler,
kwargs=kwargs,
)
self._learning_rate = learning_rate
self._optim_factory = optim_factory
self._encoder_factory = check_encoder(encoder_factory)
self._q_func_factory = check_q_func(q_func_factory)
self._n_critics = n_critics
self._target_reduction_type = target_reduction_type
self._target_update_interval = target_update_interval
self._use_gpu = check_use_gpu(use_gpu)
self._impl = impl
def _create_impl(
self, observation_shape: Sequence[int], action_size: int
) -> None:
self._impl = DQNImpl(
observation_shape=observation_shape,
action_size=action_size,
learning_rate=self._learning_rate,
optim_factory=self._optim_factory,
encoder_factory=self._encoder_factory,
q_func_factory=self._q_func_factory,
gamma=self._gamma,
n_critics=self._n_critics,
target_reduction_type=self._target_reduction_type,
use_gpu=self._use_gpu,
scaler=self._scaler,
reward_scaler=self._reward_scaler,
)
self._impl.build()
def _update(self, batch: TransitionMiniBatch) -> Dict[str, float]:
assert self._impl is not None, IMPL_NOT_INITIALIZED_ERROR
loss = self._impl.update(batch)
if self._grad_step % self._target_update_interval == 0:
self._impl.update_target()
return {"loss": loss}
[docs] def get_action_type(self) -> ActionSpace:
return ActionSpace.DISCRETE
[docs]class DoubleDQN(DQN):
r"""Double Deep Q-Network algorithm.
The difference from DQN is that the action is taken from the current Q
function instead of the target Q function.
This modification significantly decreases overestimation bias of TD
learning.
.. math::
L(\theta) = \mathbb{E}_{s_t, a_t, r_{t+1}, s_{t+1} \sim D} [(r_{t+1}
+ \gamma Q_{\theta'}(s_{t+1}, \text{argmax}_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:
* `Hasselt et al., Deep reinforcement learning with double Q-learning.
<https://arxiv.org/abs/1509.06461>`_
Args:
learning_rate (float): learning rate.
optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
optimizer factory.
encoder_factory (d3rlpy.models.encoders.EncoderFactory or str):
encoder factory.
q_func_factory (d3rlpy.models.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.
n_critics (int): the number of Q functions.
target_reduction_type (str): ensemble reduction method at target value
estimation. The available options are
``['min', 'max', 'mean', 'mix', 'none']``.
target_update_interval (int): interval to synchronize the target
network.
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']`
impl (d3rlpy.algos.torch.dqn_impl.DoubleDQNImpl):
algorithm implementation.
"""
_impl: Optional[DoubleDQNImpl]
def _create_impl(
self, observation_shape: Sequence[int], action_size: int
) -> None:
self._impl = DoubleDQNImpl(
observation_shape=observation_shape,
action_size=action_size,
learning_rate=self._learning_rate,
optim_factory=self._optim_factory,
encoder_factory=self._encoder_factory,
q_func_factory=self._q_func_factory,
gamma=self._gamma,
n_critics=self._n_critics,
target_reduction_type=self._target_reduction_type,
use_gpu=self._use_gpu,
scaler=self._scaler,
reward_scaler=self._reward_scaler,
)
self._impl.build()