Improvement of Reinforcement Learning With Supermodularity-Reference-Cited by-同舟云学术

Improvement of Reinforcement Learning With Supermodularity

Published:2023-09 Issue:9 Volume:34 Page:5298-5309
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Meng Ying¹^ORCID,Shi Fengyuan²^ORCID,Tang Lixin³^ORCID,Sun Defeng¹

Affiliation:

1. National Frontiers Science Center for Industrial Intelligence and Systems Optimization and the Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang, China

2. Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang, China

3. National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, China

Funder

Major Program of National Natural Science Foundation of China

National Natural Science Foundation of China

111 Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/10237282/10049751.pdf?arnumber=10049751

Reference43 articles.

1. Reinforcement learning for solving the vehicle routing problem;nazari;Proc 32nd Int Conf Neural Inf Process Syst,2018

2. A hybrid grouping genetic algorithm for bin packing

3. Pointer networks;vinyals;Proc Adv Neural Inf Process Syst,2015

4. Proximal policy optimization algorithms;schulman;arXiv 1707 06347,2017

5. Dueling network architectures for deep reinforcement learning;wang;Proc 33rd Int Conf Mach Learn,2016