OAC

class OAC(model, gamma=None, tau=None, alpha=None, beta=None, delta=None, actor_lr=None, critic_lr=None)

基类：parl.core.paddle.algorithm.Algorithm

__init__(model, gamma=None, tau=None, alpha=None, beta=None, delta=None, actor_lr=None, critic_lr=None)

OAC algorithm :param model: forward network of actor and critic. :type model: parl.Model :param gamma: discounted factor for reward computation :type gamma: float :param tau: decay coefficient when updating the weights of self.target_model with self.model :type tau: float :param alpha: Temperature parameter determines the relative importance of the entropy against the reward :type alpha: float :param beta: determines the relative importance of sigma_Q :type beta: float :param delta: determines the relative changes of exploration`s mean :type delta: float :param actor_lr: learning rate of the actor model :type actor_lr: float :param critic_lr: learning rate of the critic model :type critic_lr: float

learn(obs, action, reward, next_obs, terminal)

Define the loss function and create an optimizer to minize the loss.

predict(obs)

Refine the predicting process, e.g,. use the policy model to predict actions.

sample(obs)

Define the sampling process. This function returns an action with noise to perform exploration.