from .base import AlgoBase
from .dqn import DoubleDQN
from .torch.cql_impl import CQLImpl, DiscreteCQLImpl
[docs]class CQL(AlgoBase):
""" Conservative Q-Learning algorithm.
CQL is a SAC-based data-driven deep reinforcement learning algorithm, which
achieves state-of-the-art performance in offline RL problems.
CQL mitigates overestimation error by minimizing action-values under the
current policy and maximizing values under data distribution for
underestimation issue.
.. math::
L(\\theta_i) = \\alpha \\mathbb{E}_{s_t \\sim D}
[\\log{\\sum_a \\exp{Q_{\\theta_i}(s_t, a)}}
- \\mathbb{E}_{a \\sim D} [Q_{\\theta_i}(s, a)] - \\tau]
+ L_{SAC}(\\theta_i)
where :math:`\\alpha` is an automatically adjustable value via Lagrangian
dual gradient descent and :math:`\\tau` is a threshold value.
If the action-value difference is smaller than :math:`\\tau`, the
:math:`\\alpha` will become smaller.
Otherwise, the :math:`\\alpha` will become larger to aggressively penalize
action-values.
In continuous control, :math:`\\log{\\sum_a \\exp{Q(s, a)}}` is computed as
follows.
.. math::
\\log{\\sum_a \\exp{Q(s, a)}} \\approx \\log{(
\\frac{1}{2N} \\sum_{a_i \\sim \\text{Unif}(a)}^N
[\\frac{\\exp{Q(s, a_i)}}{\\text{Unif}(a)}]
+ \\frac{1}{2N} \\sum_{a_i \\sim \\pi_\\phi(a|s)}^N
[\\frac{\\exp{Q(s, a_i)}}{\\pi_\\phi(a_i|s)}])}
where :math:`N` is the number of sampled actions.
The rest of optimization is exactly same as :class:`d3rlpy.algos.SAC`.
References:
* `Kumar et al., Conservative Q-Learning for Offline Reinforcement
Learning. <https://arxiv.org/abs/2006.04779>`_
Args:
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`.
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.
update_actor_interval (int): interval to update policy function.
initial_temperature (float): initial temperature value.
initial_alpha (float): initial :math:`\\alpha` value.
alpha_threshold (float): threshold value described as :math:`\\tau`.
n_action_samples (int): the number of sampled actions to compute
:math:`\\log{\\sum_a \\exp{Q(s, a)}}`.
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.cql_impl.CQLImpl): algorithm implementation.
Attributes:
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`.
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.
update_actor_interval (int): interval to update policy function.
initial_temperature (float): initial temperature value.
initial_alpha (float): initial :math:`\\alpha` value.
alpha_threshold (float): threshold value described as :math:`\\tau`.
n_action_samples (int): the number of sampled actions to compute
:math:`\\log{\\sum_a \\exp{Q(s, a)}}`.
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.cql_impl.CQLImpl): algorithm implementation.
eval_results_ (dict): evaluation results.
"""
def __init__(self,
*,
actor_learning_rate=3e-5,
critic_learning_rate=3e-4,
temp_learning_rate=3e-5,
alpha_learning_rate=3e-4,
batch_size=100,
n_frames=1,
gamma=0.99,
tau=0.005,
n_critics=2,
bootstrap=False,
share_encoder=False,
update_actor_interval=1,
initial_temperature=1.0,
initial_alpha=5.0,
alpha_threshold=10.0,
n_action_samples=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.temp_learning_rate = temp_learning_rate
self.alpha_learning_rate = alpha_learning_rate
self.gamma = gamma
self.tau = tau
self.n_critics = n_critics
self.bootstrap = bootstrap
self.share_encoder = share_encoder
self.update_actor_interval = update_actor_interval
self.initial_temperature = initial_temperature
self.initial_alpha = initial_alpha
self.alpha_threshold = alpha_threshold
self.n_action_samples = n_action_samples
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 = CQLImpl(observation_shape=observation_shape,
action_size=action_size,
actor_learning_rate=self.actor_learning_rate,
critic_learning_rate=self.critic_learning_rate,
temp_learning_rate=self.temp_learning_rate,
alpha_learning_rate=self.alpha_learning_rate,
gamma=self.gamma,
tau=self.tau,
n_critics=self.n_critics,
bootstrap=self.bootstrap,
share_encoder=self.share_encoder,
initial_temperature=self.initial_temperature,
initial_alpha=self.initial_alpha,
alpha_threshold=self.alpha_threshold,
n_action_samples=self.n_action_samples,
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, total_step, batch):
critic_loss = self.impl.update_critic(batch.observations,
batch.actions,
batch.next_rewards,
batch.next_observations,
batch.terminals)
if total_step % self.update_actor_interval == 0:
actor_loss = self.impl.update_actor(batch.observations)
temp_loss, temp = self.impl.update_temp(batch.observations)
alpha_loss, alpha = self.impl.update_alpha(batch.observations,
batch.actions)
self.impl.update_critic_target()
self.impl.update_actor_target()
else:
actor_loss = None
temp_loss = None
temp = None
alpha_loss = None
alpha = None
return critic_loss, actor_loss, temp_loss, temp, alpha_loss, alpha
def _get_loss_labels(self):
return [
'critic_loss', 'actor_loss', 'temp_loss', 'temp', 'alpha_loss',
'alpha'
]
[docs]class DiscreteCQL(DoubleDQN):
""" Discrete version of Conservative Q-Learning algorithm.
Discrete version of CQL is a DoubleDQN-based data-driven deep reinforcement
learning algorithm (the original paper uses DQN), which achieves
state-of-the-art performance in offline RL problems.
CQL mitigates overestimation error by minimizing action-values under the
current policy and maximizing values under data distribution for
underestimation issue.
.. math::
L(\\theta) = \\mathbb{E}_{s_t \\sim D}
[\\log{\\sum_a \\exp{Q_{\\theta}(s_t, a)}}
- \\mathbb{E}_{a \\sim D} [Q_{\\theta}(s, a)]]
+ L_{DoubleDQN}(\\theta)
References:
* `Kumar et al., Conservative Q-Learning for Offline Reinforcement
Learning. <https://arxiv.org/abs/2006.04779>`_
Args:
learning_rate (float): learning rate.
batch_size (int): mini-batch size.
n_frames (int): the number of frames to stack for image observation.
gamma (float): discount factor.
n_critics (int): the number of Q functions for ensemble.
bootstrap (bool): flag to bootstrap Q functions.
eps (float): :math:`\\epsilon` for Adam optimizer.
target_update_interval (int): interval to synchronize the target
network.
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.cql_impl.DiscreteCQLImpl):
algorithm implementation.
Attributes:
learning_rate (float): learning rate.
batch_size (int): mini-batch size.
n_frames (int): the number of frames to stack for image observation.
gamma (float): discount factor.
n_critics (int): the number of Q functions for ensemble.
bootstrap (bool): flag to bootstrap Q functions.
eps (float): :math:`\\epsilon` for Adam optimizer.
target_update_interval (int): interval to synchronize the target
network.
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.CQLImpl.DiscreteCQLImpl):
algorithm implementation.
eval_results_ (dict): evaluation results.
"""
[docs] def create_impl(self, observation_shape, action_size):
self.impl = DiscreteCQLImpl(observation_shape=observation_shape,
action_size=action_size,
learning_rate=self.learning_rate,
gamma=self.gamma,
n_critics=self.n_critics,
bootstrap=self.bootstrap,
share_encoder=self.share_encoder,
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()