Question

class DDPG(model, gamma=None, tau=None, actor_lr=None, critic_lr=None)[源代码]¶

基类：Algorithm

__init__(model, gamma=None, tau=None, actor_lr=None, critic_lr=None)[源代码]¶

DDPG algorithm

参数:

• model (parl.Model) – forward network of actor and critic.

• gamma (float) – discounted factor for reward computation

• tau (float) – decay coefficient when updating the weights of self.target_model with self.model

• actor_lr (float) – learning rate of the actor model

• critic_lr (float) – learning rate of the critic model

learn(obs, action, reward, next_obs, terminal)[源代码]¶

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

predict(obs)[源代码]¶

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

sync_target(decay=None)[源代码]¶

update the target network with the training network

参数:

decay (float) – the decaying factor while updating the target network with the training network. 0 represents the assignment. None represents updating the target network slowly that depends on the hyperparameter tau.