import dataclasses
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import ActionSpace
from ...models.builders import (
create_continuous_q_function,
create_deterministic_policy,
)
from ...models.encoders import EncoderFactory, make_encoder_field
from ...models.optimizers import OptimizerFactory, make_optimizer_field
from ...models.q_functions import QFunctionFactory, make_q_func_field
from ...types import Shape
from .base import QLearningAlgoBase
from .torch.ddpg_impl import DDPGImpl, DDPGModules
__all__ = ["DDPGConfig", "DDPG"]
[docs]@dataclasses.dataclass()
class DDPGConfig(LearnableConfig):
r"""Config of 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} \Big[(r_{t+1}
+ \gamma Q_{\theta'}\big(s_{t+1}, \pi_{\phi'}(s_{t+1}))
- Q_\theta(s_t, a_t)\big)^2\Big]
.. math::
J(\phi) = \mathbb{E}_{s_t \sim D} \Big[Q_\theta\big(s_t, \pi_\phi(s_t)\big)\Big]
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:
observation_scaler (d3rlpy.preprocessing.ObservationScaler):
Observation preprocessor.
action_scaler (d3rlpy.preprocessing.ActionScaler): Action preprocessor.
reward_scaler (d3rlpy.preprocessing.RewardScaler): Reward preprocessor.
actor_learning_rate (float): Learning rate for policy function.
critic_learning_rate (float): Learning rate for Q function.
actor_optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
Optimizer factory for the actor.
critic_optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
Optimizer factory for the critic.
actor_encoder_factory (d3rlpy.models.encoders.EncoderFactory):
Encoder factory for the actor.
critic_encoder_factory (d3rlpy.models.encoders.EncoderFactory):
Encoder factory for the critic.
q_func_factory (d3rlpy.models.q_functions.QFunctionFactory):
Q function factory.
batch_size (int): Mini-batch size.
gamma (float): Discount factor.
tau (float): Target network synchronization coefficiency.
n_critics (int): Number of Q functions for ensemble.
"""
batch_size: int = 256
actor_learning_rate: float = 3e-4
critic_learning_rate: float = 3e-4
actor_optim_factory: OptimizerFactory = make_optimizer_field()
critic_optim_factory: OptimizerFactory = make_optimizer_field()
actor_encoder_factory: EncoderFactory = make_encoder_field()
critic_encoder_factory: EncoderFactory = make_encoder_field()
q_func_factory: QFunctionFactory = make_q_func_field()
tau: float = 0.005
n_critics: int = 1
[docs] def create(self, device: DeviceArg = False) -> "DDPG":
return DDPG(self, device)
@staticmethod
def get_type() -> str:
return "ddpg"
[docs]class DDPG(QLearningAlgoBase[DDPGImpl, DDPGConfig]):
def inner_create_impl(
self, observation_shape: Shape, action_size: int
) -> None:
policy = create_deterministic_policy(
observation_shape,
action_size,
self._config.actor_encoder_factory,
device=self._device,
)
targ_policy = create_deterministic_policy(
observation_shape,
action_size,
self._config.actor_encoder_factory,
device=self._device,
)
q_funcs, q_func_forwarder = create_continuous_q_function(
observation_shape,
action_size,
self._config.critic_encoder_factory,
self._config.q_func_factory,
n_ensembles=self._config.n_critics,
device=self._device,
)
targ_q_funcs, targ_q_func_forwarder = create_continuous_q_function(
observation_shape,
action_size,
self._config.critic_encoder_factory,
self._config.q_func_factory,
n_ensembles=self._config.n_critics,
device=self._device,
)
actor_optim = self._config.actor_optim_factory.create(
policy.named_modules(), lr=self._config.actor_learning_rate
)
critic_optim = self._config.critic_optim_factory.create(
q_funcs.named_modules(), lr=self._config.critic_learning_rate
)
modules = DDPGModules(
policy=policy,
targ_policy=targ_policy,
q_funcs=q_funcs,
targ_q_funcs=targ_q_funcs,
actor_optim=actor_optim,
critic_optim=critic_optim,
)
self._impl = DDPGImpl(
observation_shape=observation_shape,
action_size=action_size,
modules=modules,
q_func_forwarder=q_func_forwarder,
targ_q_func_forwarder=targ_q_func_forwarder,
gamma=self._config.gamma,
tau=self._config.tau,
device=self._device,
)
[docs] def get_action_type(self) -> ActionSpace:
return ActionSpace.CONTINUOUS
register_learnable(DDPGConfig)