NDLI: A collaborative distributed multi-agent reinforcement learning technique for dynamic agent shortest path planning via selected sub-goals in complex cluttered environments

Content Provider	IEEE Xplore Digital Library
Author	Megherbi, D.B. Kim, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Massachusetts, Lowell, MA, USA (Megherbi, D.B.; Kim, M.)
Abstract	Collaborative monitoring of large infrastructures, such as military, transportation and maritime systems are decisive issues in many surveillance, protection, and security applications. In many of these applications, dynamic multi-agent systems using reinforcement learning for agents' autonomous path planning, where agents could be moving randomly to reach their respective goals and avoiding topographical obstacles intelligently, becomes a challenging problem. This is specially so in a dynamic agent environment. In our prior work we presented an intelligent multi-agent hybrid reactive and reinforcement learning technique for collaborative autonomous agent path planning for monitoring Critical Key Infrastructures and Resources (CKIR) in a geographically and a computationally distributed systems. Here agent monitoring of large environments is reduced to monitoring of relatively smaller track-able geographically distributed agent environment regions. In this paper we tackle this problem in the challenging case of complex and cluttered environments, where agents' initial random-walk paths become challenging and relatively nonconverging. Here we propose a multi-agent distributed hybrid reactive re-enforcement learning technique based on selected agent intermediary sub-goals using a learning reward scheme in a distributed-computing memory setting. Various case study scenarios are presented for convergence study to the shortest minimum-amount-of-time exploratory steps for faster and efficient agent learning. In this work the distributed dynamic agent communication is done via a Message Passing Interface (MPI).
Starting Page	118
Ending Page	124
File Size	941295
Page Count	7
File Format	PDF
ISBN	9781479980154
DOI	10.1109/COGSIMA.2015.7108185
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Conferences Key infrastructures and resources Path planning Tracking of friendly and enemy targets Intelligent multi-agent systems Sub-goals Learning (artificial intelligence) Computer architecture Machine learning Distributed systems and networks Peer-to-peer computing Monitoring Multi-agent systems
Content Type	Text
Resource Type	Article

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