from .base import AlgoBase
from .torch.bcq_impl import BCQImpl, DiscreteBCQImpl
from ..optimizers import AdamFactory
from ..argument_utils import check_encoder
from ..argument_utils import check_use_gpu
from ..argument_utils import check_augmentation
from ..argument_utils import check_q_func
[docs]class BCQ(AlgoBase):
r""" Batch-Constrained Q-learning algorithm.
BCQ is the very first practical data-driven deep reinforcement learning
lgorithm.
The major difference from DDPG is that the policy function is represented
as combination of conditional VAE and perturbation function in order to
remedy extrapolation error emerging from target value estimation.
The encoder and the decoder of the conditional VAE is represented as
:math:`E_\omega` and :math:`D_\omega` respectively.
.. math::
L(\omega) = E_{s_t, a_t \sim D} [(a - \tilde{a})^2
+ D_{KL}(N(\mu, \sigma)|N(0, 1))]
where :math:`\mu, \sigma = E_\omega(s_t, a_t)`,
:math:`\tilde{a} = D_\omega(s_t, z)` and :math:`z \sim N(\mu, \sigma)`.
The policy function is represented as a residual function
with the VAE and the perturbation function represented as
:math:`\xi_\phi (s, a)`.
.. math::
\pi(s, a) = a + \Phi \xi_\phi (s, a)
where :math:`a = D_\omega (s, z)`, :math:`z \sim N(0, 0.5)` and
:math:`\Phi` is a perturbation scale designated by `action_flexibility`.
Although the policy is learned closely to data distribution, the
perturbation function can lead to more rewarded states.
BCQ also leverages twin Q functions and computes weighted average over
maximum values and minimum values.
.. math::
L(\theta_i) = \mathbb{E}_{s_t, a_t, r_{t+1}, s_{t+1} \sim D}
[(y - Q_{\theta_i}(s_t, a_t))^2]
.. math::
y = r_{t+1} + \gamma \max_{a_i} [
\lambda \min_j Q_{\theta_j'}(s_{t+1}, a_i)
+ (1 - \lambda) \max_j Q_{\theta_j'}(s_{t+1}, a_i)]
where :math:`\{a_i \sim D(s_{t+1}, z), z \sim N(0, 0.5)\}_{i=1}^n`.
The number of sampled actions is designated with `n_action_samples`.
Finally, the perturbation function is trained just like DDPG's policy
function.
.. math::
J(\phi) = \mathbb{E}_{s_t \sim D, a_t \sim D_\omega(s_t, z),
z \sim N(0, 0.5)}
[Q_{\theta_1} (s_t, \pi(s_t, a_t))]
At inference time, action candidates are sampled as many as
`n_action_samples`, and the action with highest value estimation is taken.
.. math::
\pi'(s) = \text{argmax}_{\pi(s, a_i)} Q_{\theta_1} (s, \pi(s, a_i))
Note:
The greedy action is not deterministic because the action candidates
are always randomly sampled. This might affect `save_policy` method and
the performance at production.
References:
* `Fujimoto et al., Off-Policy Deep Reinforcement Learning without
Exploration. <https://arxiv.org/abs/1812.02900>`_
Args:
actor_learning_rate (float): learning rate for policy function.
critic_learning_rate (float): learning rate for Q functions.
imitator_learning_rate (float): learning rate for Conditional VAE.
actor_optim_factory (d3rlpy.optimizers.OptimizerFactory):
optimizer factory for the actor.
critic_optim_factory (d3rlpy.optimizers.OptimizerFactory):
optimizer factory for the critic.
imitator_optim_factory (d3rlpy.optimizers.OptimizerFactory):
optimizer factory for the conditional VAE.
actor_encoder_factory (d3rlpy.encoders.EncoderFactory or str):
encoder factory for the actor.
critic_encoder_factory (d3rlpy.encoders.EncoderFactory or str):
encoder factory for the critic.
imitator_encoder_factory (d3rlpy.encoders.EncoderFactory or str):
encoder factory for the conditional VAE.
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.
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.
lam (float): weight factor for critic ensemble.
n_action_samples (int): the number of action samples to estimate
action-values.
action_flexibility (float): output scale of perturbation function
represented as :math:`\Phi`.
rl_start_epoch (int): epoch to start to update policy function and Q
functions. If this is large, RL training would be more stabilized.
latent_size (int): size of latent vector for Conditional VAE.
beta (float): KL reguralization term for Conditional VAE.
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.bcq_impl.BCQImpl): algorithm implementation.
Attributes:
actor_learning_rate (float): learning rate for policy function.
critic_learning_rate (float): learning rate for Q functions.
imitator_learning_rate (float): learning rate for Conditional VAE.
actor_optim_factory (d3rlpy.optimizers.OptimizerFactory):
optimizer factory for the actor.
critic_optim_factory (d3rlpy.optimizers.OptimizerFactory):
optimizer factory for the critic.
imitator_optim_factory (d3rlpy.optimizers.OptimizerFactory):
optimizer factory for the conditional VAE.
actor_encoder_factory (d3rlpy.encoders.EncoderFactory):
encoder factory for the actor.
critic_encoder_factory (d3rlpy.encoders.EncoderFactory):
encoder factory for the critic.
imitator_encoder_factory (d3rlpy.encoders.EncoderFactory):
encoder factory for the conditional VAE.
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.
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.
lam (float): weight factor for critic ensemble.
n_action_samples (int): the number of action samples to estimate
action-values.
action_flexibility (float): output scale of perturbation function.
rl_start_epoch (int): epoch to start to update policy function and Q
functions.
latent_size (int): size of latent vector for Conditional VAE.
beta (float): KL reguralization term for Conditional VAE.
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.bcq_impl.BCQImpl): algorithm implementation.
eval_results_ (dict): evaluation results.
"""
def __init__(self,
*,
actor_learning_rate=1e-3,
critic_learning_rate=1e-3,
imitator_learning_rate=1e-3,
actor_optim_factory=AdamFactory(),
critic_optim_factory=AdamFactory(),
imitator_optim_factory=AdamFactory(),
actor_encoder_factory='default',
critic_encoder_factory='default',
imitator_encoder_factory='default',
q_func_factory='mean',
batch_size=100,
n_frames=1,
n_steps=1,
gamma=0.99,
tau=0.005,
n_critics=2,
bootstrap=False,
share_encoder=False,
update_actor_interval=1,
lam=0.75,
n_action_samples=100,
action_flexibility=0.05,
rl_start_epoch=0,
latent_size=32,
beta=0.5,
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.actor_learning_rate = actor_learning_rate
self.critic_learning_rate = critic_learning_rate
self.imitator_learning_rate = imitator_learning_rate
self.actor_optim_factory = actor_optim_factory
self.critic_optim_factory = critic_optim_factory
self.imitator_optim_factory = imitator_optim_factory
self.actor_encoder_factory = check_encoder(actor_encoder_factory)
self.critic_encoder_factory = check_encoder(critic_encoder_factory)
self.imitator_encoder_factory = check_encoder(imitator_encoder_factory)
self.q_func_factory = check_q_func(q_func_factory)
self.tau = tau
self.n_critics = n_critics
self.bootstrap = bootstrap
self.share_encoder = share_encoder
self.update_actor_interval = update_actor_interval
self.lam = lam
self.n_action_samples = n_action_samples
self.action_flexibility = action_flexibility
self.rl_start_epoch = rl_start_epoch
self.latent_size = latent_size
self.beta = beta
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 = BCQImpl(
observation_shape=observation_shape,
action_size=action_size,
actor_learning_rate=self.actor_learning_rate,
critic_learning_rate=self.critic_learning_rate,
imitator_learning_rate=self.imitator_learning_rate,
actor_optim_factory=self.actor_optim_factory,
critic_optim_factory=self.critic_optim_factory,
imitator_optim_factory=self.imitator_optim_factory,
actor_encoder_factory=self.actor_encoder_factory,
critic_encoder_factory=self.critic_encoder_factory,
imitator_encoder_factory=self.imitator_encoder_factory,
q_func_factory=self.q_func_factory,
gamma=self.gamma,
tau=self.tau,
n_critics=self.n_critics,
bootstrap=self.bootstrap,
share_encoder=self.share_encoder,
lam=self.lam,
n_action_samples=self.n_action_samples,
action_flexibility=self.action_flexibility,
latent_size=self.latent_size,
beta=self.beta,
use_gpu=self.use_gpu,
scaler=self.scaler,
augmentation=self.augmentation)
self.impl.build()
[docs] def update(self, epoch, total_step, batch):
imitator_loss = self.impl.update_imitator(batch.observations,
batch.actions)
if epoch >= self.rl_start_epoch:
critic_loss = self.impl.update_critic(
batch.observations, batch.actions, batch.next_rewards,
batch.next_observations, batch.terminals, batch.n_steps)
if total_step % self.update_actor_interval == 0:
actor_loss = self.impl.update_actor(batch.observations)
self.impl.update_actor_target()
self.impl.update_critic_target()
else:
actor_loss = None
else:
critic_loss = None
actor_loss = None
return critic_loss, actor_loss, imitator_loss
[docs] def sample_action(self, x):
""" BCQ does not support sampling action.
"""
raise NotImplementedError('BCQ does not support sampling action.')
def _get_loss_labels(self):
return ['critic_loss', 'actor_loss', 'imitator_loss']
[docs]class DiscreteBCQ(AlgoBase):
r""" Discrete version of Batch-Constrained Q-learning algorithm.
Discrete version takes theories from the continuous version, but the
algorithm is much simpler than that.
The imitation function :math:`G_\omega(a|s)` is trained as supervised
learning just like Behavior Cloning.
.. math::
L(\omega) = \mathbb{E}_{a_t, s_t \sim D}
[-\sum_a p(a|s_t) \log G_\omega(a|s_t)]
With this imitation function, the greedy policy is defined as follows.
.. math::
\pi(s_t) = \text{argmax}_{a|G_\omega(a|s_t)
/ \max_{\tilde{a}} G_\omega(\tilde{a}|s_t) > \tau}
Q_\theta (s_t, a)
which eliminates actions with probabilities :math:`\tau` times smaller
than the maximum one.
Finally, the loss function is computed in Double DQN style with the above
constrained policy.
.. 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(s_{t+1}))
- Q_\theta(s_t, a_t))^2]
References:
* `Fujimoto et al., Off-Policy Deep Reinforcement Learning without
Exploration. <https://arxiv.org/abs/1812.02900>`_
* `Fujimoto et al., Benchmarking Batch Deep Reinforcement Learning
Algorithms. <https://arxiv.org/abs/1910.01708>`_
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 for ensemble.
bootstrap (bool): flag to bootstrap Q functions.
share_encoder (bool): flag to share encoder network.
action_flexibility (float): probability threshold represented as
:math:`\tau`.
beta (float): reguralization term for imitation function.
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.bcq_impl.DiscreteBCQImpl):
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.
action_flexibility (float): probability threshold represented as
:math:`\tau`.
beta (float): reguralization term for imitation function.
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.bcq_impl.DiscreteBCQImpl):
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,
action_flexibility=0.3,
beta=0.5,
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.action_flexibility = action_flexibility
self.beta = beta
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 = DiscreteBCQImpl(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,
action_flexibility=self.action_flexibility,
beta=self.beta,
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 ['loss']