from .base import AlgoBase
from .torch.ddpg_impl import DDPGImpl
[docs]class DDPG(AlgoBase):
""" Deep Deterministic Policy Gradients algorithm.
DDPG is an actor-critic algorithm that trains a Q function parametrized
with :math:`\\theta` and a policy function parametrized with :math:`\\phi`.
.. 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}, \\pi_{\\phi'}(s_{t+1}))
- Q_\\theta(s_t, a_t))^2]
.. math::
J(\\phi) = \\mathbb{E}_{s_t \\sim D} [Q_\\theta(s_t, \\pi_\\phi(s_t))]
where :math:`\\theta'` and :math:`\\phi` are the target network parameters.
There target network parameters are updated every iteration.
.. math::
\\theta' \\gets \\tau \\theta + (1 - \\tau) \\theta'
\\phi' \\gets \\tau \\phi + (1 - \\tau) \\phi'
References:
* `Silver et al., Deterministic policy gradient algorithms.
<http://proceedings.mlr.press/v32/silver14.html>`_
* `Lillicrap et al., Continuous control with deep reinforcement
learning. <https://arxiv.org/abs/1509.02971>`_
Args:
actor_learning_rate (float): learning rate for policy function.
critic_learning_rate (float): learning rate for Q function.
batch_size (int): mini-batch size.
n_frames (int): the number of frames to stack for image observation.
gamma (float): discount factor.
tau (float): target network synchronization coefficiency.
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.
reguralizing_rate (float): reguralizing term for policy function.
eps (float): :math:`\\epsilon` for Adam optimizer.
use_batch_norm (bool): flag to insert batch normalization layers.
q_func_type (str): type of Q function. Available options are
`['mean', 'qr', 'iqn', 'fqf']`.
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.
n_augmentations (int): the number of data augmentations to update.
encoder_params (dict): optional arguments for encoder setup. If the
observation is pixel, you can pass ``filters`` with list of tuples
consisting with ``(filter_size, kernel_size, stride)`` and
``feature_size`` with an integer scaler for the last linear layer
size. If the observation is vector, you can pass ``hidden_units``
with list of hidden unit sizes.
dynamics (d3rlpy.dynamics.base.DynamicsBase): dynamics model for data
augmentation.
impl (d3rlpy.algos.torch.ddpg_impl.DDPGImpl): algorithm implementation.
Attributes:
actor_learning_rate (float): learning rate for policy function.
critic_learning_rate (float): learning rate for Q function.
batch_size (int): mini-batch size.
n_frames (int): the number of frames to stack for image observation.
gamma (float): discount factor.
tau (float): target network synchronization coefficiency.
n_critics (int): the number of Q functions for ensemble.
bootstrap (bool): flag to bootstraep Q functions.
share_encoder (bool): flag to share encoder network.
reguralizing_rate (float): reguralizing term for policy function.
eps (float): :math:`\\epsilon` for Adam optimizer.
use_batch_norm (bool): flag to insert batch normalization layers.
q_func_type (str): type of Q function.
use_gpu (d3rlpy.gpu.Device): GPU device.
scaler (d3rlpy.preprocessing.Scaler): preprocessor.
augmentation (d3rlpy.augmentation.AugmentationPipeline):
augmentation pipeline.
n_augmentations (int): the number of data augmentations to update.
encoder_params (dict): optional arguments for encoder setup.
dynamics (d3rlpy.dynamics.base.DynamicsBase): dynamics model.
impl (d3rlpy.algos.torch.ddpg_impl.DDPGImpl): algorithm implementation.
eval_results_ (dict): evaluation results.
"""
def __init__(self,
*,
actor_learning_rate=3e-4,
critic_learning_rate=3e-4,
batch_size=100,
n_frames=1,
gamma=0.99,
tau=0.005,
n_critics=1,
bootstrap=False,
share_encoder=False,
reguralizing_rate=1e-10,
eps=1e-8,
use_batch_norm=False,
q_func_type='mean',
use_gpu=False,
scaler=None,
augmentation=[],
n_augmentations=1,
encoder_params={},
dynamics=None,
impl=None,
**kwargs):
super().__init__(batch_size=batch_size,
n_frames=n_frames,
scaler=scaler,
augmentation=augmentation,
dynamics=dynamics,
use_gpu=use_gpu)
self.actor_learning_rate = actor_learning_rate
self.critic_learning_rate = critic_learning_rate
self.gamma = gamma
self.tau = tau
self.n_critics = n_critics
self.bootstrap = bootstrap
self.share_encoder = share_encoder
self.reguralizing_rate = reguralizing_rate
self.eps = eps
self.use_batch_norm = use_batch_norm
self.q_func_type = q_func_type
self.n_augmentations = n_augmentations
self.encoder_params = encoder_params
self.impl = impl
[docs] def create_impl(self, observation_shape, action_size):
self.impl = DDPGImpl(observation_shape=observation_shape,
action_size=action_size,
actor_learning_rate=self.actor_learning_rate,
critic_learning_rate=self.critic_learning_rate,
gamma=self.gamma,
tau=self.tau,
n_critics=self.n_critics,
bootstrap=self.bootstrap,
share_encoder=self.share_encoder,
reguralizing_rate=self.reguralizing_rate,
eps=self.eps,
use_batch_norm=self.use_batch_norm,
q_func_type=self.q_func_type,
use_gpu=self.use_gpu,
scaler=self.scaler,
augmentation=self.augmentation,
n_augmentations=self.n_augmentations,
encoder_params=self.encoder_params)
self.impl.build()
[docs] def update(self, epoch, itr, batch):
critic_loss = self.impl.update_critic(batch.observations,
batch.actions,
batch.next_rewards,
batch.next_observations,
batch.terminals)
actor_loss = self.impl.update_actor(batch.observations)
self.impl.update_critic_target()
self.impl.update_actor_target()
return critic_loss, actor_loss
def _get_loss_labels(self):
return ['critic_loss', 'actor_loss']