from .base import AlgoBase
from .torch.dqn_impl import DQNImpl, DoubleDQNImpl
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 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.optimizers.OptimizerFactory or str):
optimizer factory.
encoder_factory (d3rlpy.encoders.EncoderFactory or str):
encoder factory.
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.
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.
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']`
augmentation (d3rlpy.augmentation.AugmentationPipeline or list(str)):
augmentation pipeline.
dynamics (d3rlpy.dynamics.base.DynamicsBase): dynamics model for data
augmentation.
impl (d3rlpy.algos.torch.dqn_impl.DQNImpl): algorithm implementation.
Attributes:
learning_rate (float): learning rate.
optim_factory (d3rlpy.optimizers.OptimizerFactory): optimizer factory.
encoder_factory (d3rlpy.encoders.EncoderFactory): encoder factory.
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.
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.
target_update_interval (int): interval to update the target network.
use_gpu (d3rlpy.gpu.Device): GPU device.
scaler (d3rlpy.preprocessing.Scaler): preprocessor.
augmentation (d3rlpy.augmentation.AugmentationPipeline):
augmentation pipeline.
dynamics (d3rlpy.dynamics.base.DynamicsBase): dynamics model.
impl (d3rlpy.algos.torch.dqn_impl.DQNImpl): algorithm implementation.
eval_results_ (dict): evaluation results.
"""
def __init__(self,
*,
learning_rate=6.25e-5,
optim_factory=AdamFactory(),
encoder_factory='default',
q_func_factory='mean',
batch_size=32,
n_frames=1,
n_steps=1,
gamma=0.99,
n_critics=1,
bootstrap=False,
share_encoder=False,
target_update_interval=8e3,
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.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.bootstrap = bootstrap
self.share_encoder = share_encoder
self.target_update_interval = target_update_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 = 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,
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):
loss = self.impl.update(batch.observations, batch.actions,
batch.next_rewards, batch.next_observations,
batch.terminals, batch.n_steps)
if total_step % self.target_update_interval == 0:
self.impl.update_target()
return (loss, )
def _get_loss_labels(self):
return ['value_loss']
[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.optimizers.OptimizerFactory): optimizer factory.
encoder_factory (d3rlpy.encoders.EncoderFactory or str):
encoder factory.
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.
n_critics (int): the number of Q functions.
bootstrap (bool): flag to bootstrap Q functions.
share_encoder (bool): flag to share encoder network.
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']`
augmentation (d3rlpy.augmentation.AugmentationPipeline or list(str)):
augmentation pipeline.
dynamics (d3rlpy.dynamics.base.DynamicsBase): dynamics model for data
augmentation.
impl (d3rlpy.algos.torch.dqn_impl.DoubleDQNImpl):
algorithm implementation.
Attributes:
learning_rate (float): learning rate.
optim_factory (d3rlpy.optimizers.OptimizerFactory): optimizer factory.
encoder_factory (d3rlpy.encoders.EncoderFactory): encoder factory.
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.
n_critics (int): the number of Q functions.
bootstrap (bool): flag to bootstrap Q functions.
share_encoder (bool): flag to share encoder network.
target_update_interval (int): interval to synchronize the target
network.
use_gpu (d3rlpy.gpu.Device): GPU device.
scaler (d3rlpy.preprocessing.Scaler): preprocessor.
augmentation (d3rlpy.augmentation.AugmentationPipeline or list(str)):
augmentation pipeline.
dynamics (d3rlpy.dynaics.base.DynamicsBase): dynamics model.
impl (d3rlpy.algos.torch.dqn_impl.DoubleDQNImpl):
algorithm implementation.
"""
[docs] def create_impl(self, observation_shape, action_size):
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,
bootstrap=self.bootstrap,
share_encoder=self.share_encoder,
use_gpu=self.use_gpu,
scaler=self.scaler,
augmentation=self.augmentation)
self.impl.build()