Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain-Reference-Cited by-同舟云学术

Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain

Published:2023 Issue: Volume: Page:1-21
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Hao Jianye¹^ORCID,Yang Tianpei¹^ORCID,Tang Hongyao¹^ORCID,Bai Chenjia²,Liu Jinyi¹^ORCID,Meng Zhaopeng¹,Liu Peng³^ORCID,Wang Zhen⁴^ORCID

Affiliation:

1. College of Intelligence and Computing, Tianjin University, Tianjin, China

2. Shanghai Artificial Intelligence Laboratory, Shanghai, China

3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

4. OPtics and Electronics (iOPEN) and the School of Cyberspace, School of Artificial Intelligence, Northwestern Polytechnical University, Xi’an, China

Funder

National Science Fund for Distinguished Young Scholars

National Natural Science Foundation of China

New Generation of Artificial Intelligence Science and Technology Major Project of Tianjin

Tencent Foundation and XPLORER PRIZE

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10021988.pdf?arnumber=10021988

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