Multi-Agent Reinforcement Learning System Using Multi-Agent Proximal Policy Optimizer Algorithm in SMAC Environment-Reference-Cited by-同舟云学术

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Multi-Agent Reinforcement Learning System Using Multi-Agent Proximal Policy Optimizer Algorithm in SMAC Environment

Published:2023-07-24 Issue: Volume:8 Page:357-360
ISSN:
Container-title:2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)
language:
Short-container-title:

Author:

Abdulghani Abdulghani M.¹,Abdulghani Mokhles M.¹,Walters Wilbur L.¹,Abed Khalid H.¹

Affiliation:

1. Jackson State University,Jackson,Mississippi

Funder

United States Air Force Office of Scientific Research (AFOSR)

Jackson State University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10487132/10487174/10487484.pdf?arnumber=10487484

Reference15 articles.

1. Implementing the Deep Q-Network;Roderick;ArXiv,2017

2. Safe, multi-agent, reinforcement learning for autonomous driving;Shalev-Shwartz;ArXiv

3. The Surprising Effectiveness of MAPPO in Cooperative, Multi-Agent Games;Yu;ArXiv,2021

4. The Hanabi challenge: A new frontier for AI research

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