A collaborative multi-agent reinforcement learning approach based on reward adaptive allocation

Zhenjie Wang

doi:10.1117/12.3051844

18 November 2024 A collaborative multi-agent reinforcement learning approach based on reward adaptive allocation

Zhenjie Wang

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 1340312 (2024) https://doi.org/10.1117/12.3051844
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

This article analyzes the current research status of multi-agent reinforcement learning from the perspective of the training architecture and reward allocation of multi-agent reinforcement learning. From the analysis of the interaction process between fully cooperative multi-agent and environment, it can be seen that the focus is on solving the problem of reward allocation. However, common methods have problems such as low training efficiency, insufficient collaboration ability between multi-agent systems, and inability to adapt to large-scale scenarios. Therefore, this article proposes a collaborative multi-agent reinforcement learning method based on reward adaptive allocation, lists specific steps, and according to the analysis of experimental results, it can be concluded that this method can achieve higher and less volatile environmental rewards compared to MADDPG. Therefore, this method can explore samples more efficiently and improve training efficiency through the guidance of reward functions. In addition, the constraints of reward functions can enhance the stability of decision-making.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenjie Wang "A collaborative multi-agent reinforcement learning approach based on reward adaptive allocation", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 1340312 (18 November 2024); https://doi.org/10.1117/12.3051844

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available