Mutli-agent consensus under communication failure using Actor-Critic Reinforcement Learning

被引：0

作者：

Kandath, Harikumar ^{[1
]}

Senthilnath, J. ^{[2
]}

Sundaram, Suresh ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore

[2] ASTAR, Inst Infocomm Res, Singapore 138632, Singapore

来源：

2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI) | 2018年

关键词：

Actor; Consensus; Critic; Neural network; Reinforcement learning; MULTIAGENT SYSTEMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper addresses the problem of achieving multi-agent consensus under sudden total communication failure. The agents are assumed to be moving along the periphery of a circle. The proposed solution uses the actor-critic reinforcement learning method to achieve consensus, when there is no communication between the agents. A performance index is defined that take into consider the difference in angular position between the neighbouring agents. The actions of each agent while achieving consensus with full communication is learned by an actor neural network, while the critic neural network learns to predict the performance index. The proposed solution is validated by a numerical simulation with live agents moving along the periphery of a circle.

引用

页码：1461 / 1465

页数：5

共 50 条

[1] A multi-agent reinforcement learning using Actor-Critic methods
Li, Chun-Gui
Wang, Meng
Yuan, Qing-Neng
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2008, : 878 - 882
[2] Multi-Agent Actor-Critic Multitask Reinforcement Learning based on GTD(1) with Consensus
Stankovic, Milo S. S.
Beko, Marko
Ilic, Nemanja
Stankovic, Srdjan S.
2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), 2022, : 4591 - 4596
[3] Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning
Joseph, Geethu
Gursoy, M. Cenk
Varshney, Pramod K.
PROCEEDINGS OF THE 21ST IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (IEEE SPAWC2020), 2020,
[4] A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning
Lin, Yixuan
Zhang, Kaiqing
Yang, Zhuoran
Wang, Zhaoran
Basar, Tamer
Sandhu, Romeil
Liu, Ji
2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC), 2019, : 5562 - 5567
[5] Actor-Critic Algorithms for Constrained Multi-agent Reinforcement Learning
Diddigi, Raghuram Bharadwaj
Reddy, D. Sai Koti
Prabuchandran, K. J.
Bhatnagar, Shalabh
AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, 2019, : 1931 - 1933
[6] Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms
Prashant Trivedi
Nandyala Hemachandra
Dynamic Games and Applications, 2023, 13 : 25 - 55
[7] Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Christianos, Filippos
Schafer, Lukas
Albrecht, Stefano V.
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
[8] Dynamic Pricing Based on Demand Response Using Actor-Critic Agent Reinforcement Learning
Ismail, Ahmed
Baysal, Mustafa
ENERGIES, 2023, 16 (14)
[9] Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms
Trivedi, Prashant
Hemachandra, Nandyala
DYNAMIC GAMES AND APPLICATIONS, 2023, 13 (01) : 25 - 55
[10] Distributed Multi-Agent Reinforcement Learning by Actor-Critic Method
Heredia, Paulo C.
Mou, Shaoshuai
IFAC PAPERSONLINE, 2019, 52 (20): : 363 - 368

← 1 2 3 4 5 →