Browse > Article
http://dx.doi.org/10.9708/jksci.2021.26.03.009

A Study of Collaborative and Distributed Multi-agent Path-planning using Reinforcement Learning  

Kim, Min-Suk (Dept. of Human Intelligence and Robot Engineering, Sangmyung University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.3, 2021 , pp. 9-17 More about this Journal
Abstract
In this paper, an autonomous multi-agent path planning using reinforcement learning for monitoring of infrastructures and resources in a computationally distributed system was proposed. Reinforcement-learning-based multi-agent exploratory system in a distributed node enable to evaluate a cumulative reward every action and to provide the optimized knowledge for next available action repeatedly by learning process according to a learning policy. Here, the proposed methods were presented by (a) approach of dynamics-based motion constraints multi-agent path-planning to reduce smaller agent steps toward the given destination(goal), where these agents are able to geographically explore on the environment with initial random-trials versus optimal-trials, (b) approach using agent sub-goal selection to provide more efficient agent exploration(path-planning) to reach the final destination(goal), and (c) approach of reinforcement learning schemes by using the proposed autonomous and asynchronous triggering of agent exploratory phases.
Keywords
Reinforcement Learning; Multi-agent; Sub-goal; Sharing Information; Collaborative;
Citations & Related Records
연도 인용수 순위
  • Reference
1 D. B. Megherbi, D. C. Xu, "Multi-Agent Distributed Dynamic Scheduling for Large Distributed Critical Key Infrastructures and Resources (CKIR) Surveillance and Monitoring", in Proceeding of IEEE International Conference on Technology for Homeland Security(HST), 2011. DOI: 10.1109/THS.2011.6107907   DOI
2 K. Zhang, Z. Yang, and T. Basar, "Networked Multi-Agent Reinforcement Learning in Continuous Spaces", in Proceeding of 2018 IEEE Conference on Decision and Control (CDC), 2018.DOI: 10.1109/CDC.2018.8619581   DOI
3 D. B. Megherbi, P. Levesque, "A Distributed Multi-Agent Tracking, awareness, and communication System Architecture for Synchronized Real-Time Situational Understanding, Surveillance, Decision-Making, and Control", in Proceeding of IEEE International Conference on Technology for Homeland Security(HST), 2009. DOI: 10.1109/THS.2010.5654983   DOI
4 D. B. Megherbi, Radumilo-Franklin, Jelena, "An Intelligent Multi-agent Distributed Battlefield via Multi-Token Message Passing", in Proceeding of IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2009. DOI: 10.1109/CIMSA.2009.5069929   DOI
5 J. Soler, V. Julian, M. Rebollo, C. Carrascosa, V. Botti., "Towards a Real-Time Multi-Agent System Architecture", Universidad Politecnica de Valencia, Valencia, Spain, 2002.
6 B. Horling, V. Lesser, R. Vincent, T. Wagner, "The Soft Real Time Agent Control Architecture", UMASS Department of Computer Science Technical Report WS-02-15, USA, 2002.
7 Stuart Russell, Peter Norvig, "Artificial Intelligence", A Modern Approach 2nd edition, Prentice Hall, 2003.
8 Xue Jinlin, Gao Qiang, Ju Weiping, "Reinforcement Learning for Engine Idle Speed Control", in Proceeding of 2010 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), 2010. DOI: 10.1109/ICMTMA.2010.249   DOI
9 M. Madera, D. B. Megherbi, "An Interconnected Dynamical System Composed of Dynamics-based Reinforcement Learning Agents in a Distributed Environment: A Case Study", in Proceeding of IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2012. DOI: 10.1109/CIMSA.2012.6269597   DOI
10 D. B. Megherbi, M. Madera, "A hybrid P2P and master-slave architecture for intelligent multi-agent reinforcement learning in a distributed computing environment: A case study", in Proceeding of IEEE International Conference, Computational Intelligence for Measurement Systems and Applications (CIMSA), 2010. DOI: 10.1109/CIMSA.2010.5611770   DOI
11 W. M. Zuberek, "Performance Limitations of Block-Multithread ed Distributed-Memory System", in Proceeding of the Winter Simulation Conference(WSC), 2009. DOI: 10.1109/WSC.2009.5429718   DOI
12 D. B. Megherbi, V. Malaya, "A Hybrid Cognitive/Reactive Intelligent Agent Autonomous Path Planning Technique in a Networked-Distributed Unstructured Environment for Reinforcement Learning", The Journal of Supercomputing, Vol. 59, Issue3, pp.1188-1217, 2012.   DOI
13 M.R Shaker, S. Yue, T. Duckett, "Vision-based reinforcement learning using approximate policy iteration", in Proceeding of 2009 International Conference, 2009.
14 J. JIANG, S. Zhao-Pin, Q. Mei-Bin, G. ZHANG, "Multi-task Coalition Parallel Formation Strategy Based on Reinforcement Learning", Acta Automatica Sinica, Vol.34, No.3, pp.349-352, 2008.   DOI
15 D. B. Megherbi, M. Kim, "A Collaborative Distributed Multi-Agent Reinforcement Learning Technique for Dynamic Agent Shortest Path Planning via Selected Sub-goals in Complex Cluttered Environments", in Proceeding of IEEE Conference, CogSIMA, 2015.
16 A. Sharma, S. Gu, S. Levine, V. Kumar, K. Hausman, "DADS: Unsupervised Reinforcement Learning for Skill Discovery", posted by AI Resident, Google Research at the Google Brain team and the Robotics at Google team, May. 2020.
17 C. Picus, L. Cambrini, W. Herzner, "Boltzmann Machine Topology Learning for Distributed Sensor Networks Using Loopy Belief Propagation Inference. Machine Learning and Applications", in Proceeding of 2008th Seventh International Conference, ICMLA, 2008. DOI: 10.1109/ICMLA.2008.60   DOI
18 D. B. Megherbi, M. Kim, M. Madera, "A Study of Collaborative Distributed Multi-Goal and Multi-agent based Systems for Large Critical Key Infrastructures and Resources (CKIR) Dynamic Monitoring and Surveillance", in Proceeding of IEEE International Conference on Technologies for Homeland Security, 2013. DOI: 10.1109/THS.2013.6699087   DOI
19 J. Kim, H. Lim, C. Kim, M. Kim, Y. Hong, Y. Han, "Imitation Reinforcement Learning-Based Remote Rotary Inverted Pendulum Control in OpenFlow Network" Published in IEEE Access, Vol. 7, 2019.