A policy optimization algorithm based on sample adaptive reuse and dual-clipping for robotic action control-Reference-Cited by-同舟云学术

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A policy optimization algorithm based on sample adaptive reuse and dual-clipping for robotic action control

Published:2023-02 Issue: Volume:134 Page:109967
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Zhao Li-yang^ORCID,Chang Tian-qing,Zhang Jie,Zhang Lei,Chu Kai-xuan,Guo Li-bin,Kong De-peng

Publisher

Elsevier BV

Subject

Software

Reference39 articles.

1. Human-level control through deep reinforcement learning;Mnih;Nature,2015

2. Mastering the game of go without human knowledge;Silver;Nature,2017

3. Mastering the game of go with deep neural networks and tree search;Silver;Nature,2016

4. Model-free trajectory-based policy optimization with monotonic improvement;Akrour;J. Mach. Learn. Res.,2018

5. Data-driven robust control of discrete-time uncertain linear systems via off-policy reinforcement learning;ang;IEEE Trans. Neural Netw. Learn. Syst.,2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Effect of Number of Lanes on Traffic Characteristics of Reinforcement Learning Based Autonomous Driving;IEEE Access;2023

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