Deep Deterministic Policy Gradient Based on Double Network Prioritized Experience Replay

Deep Deterministic Policy Gradient Based on Double Network Prioritized Experience Replay

The traditional deep deterministic policy gradient (DDPG) algorithm has the disadvantages of slow convergence velocity and ease of falling into the local optimum. From these two perspectives, a DDPG algorithm based on the double network prioritized experience replay mechanism (DNPER-DDPG) is propose...

Full description

Bibliographic Details
Main Authors: Chaohai Kang, Chuiting Rong, Weijian Ren, Fengcai Huo, Pengyun Liu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Continuous action space
deep deterministic policy gradient
experience replay mechanism
function approximation error
priority division
Online Access:https://ieeexplore.ieee.org/document/9409070/

Similar Items

Similar Items