NDLI: Cooperative reinforcement learning in topology-based multi-agent systems

Content Provider	Springer Nature Link
Author	Xiao, Dan Tan, Ah Hwee
Copyright Year	2011
Abstract	Topology-based multi-agent systems (TMAS), wherein agents interact with one another according to their spatial relationship in a network, are well suited for problems with topological constraints. In a TMAS system, however, each agent may have a different state space, which can be rather large. Consequently, traditional approaches to multi-agent cooperative learning may not be able to scale up with the complexity of the network topology. In this paper, we propose a cooperative learning strategy, under which autonomous agents are assembled in a binary tree formation (BTF). By constraining the interaction between agents, we effectively unify the state space of individual agents and enable policy sharing across agents. Our complexity analysis indicates that multi-agent systems with the BTF have a much smaller state space and a higher level of flexibility, compared with the general form of n-ary (n > 2) tree formation. We have applied the proposed cooperative learning strategy to a class of reinforcement learning agents known as temporal difference-fusion architecture for learning and cognition (TD-FALCON). Comparative experiments based on a generic network routing problem, which is a typical TMAS domain, show that the TD-FALCON BTF teams outperform alternative methods, including TD-FALCON teams in single agent and n-ary tree formation, a Q-learning method based on the table lookup mechanism, as well as a classical linear programming algorithm. Our study further shows that TD-FALCON BTF can adapt and function well under various scales of network complexity and traffic volume in TMAS domains.
Starting Page	86
Ending Page	119
Page Count	34
File Format	PDF
ISSN	13872532
Journal	Autonomous Agents and Multi-Agent Systems
Volume Number	26
Issue Number	1
e-ISSN	15737454
Language	English
Publisher	Springer US
Publisher Date	2011-10-07
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Topology-based multi-agent systems Cooperative learning Reinforcement learning Binary tree formation Policy sharing Artificial Intelligence (incl. Robotics) Computer Systems Organization and Communication Networks Computing Methodologies Software Engineering/Programming and Operating Systems User Interfaces and Human Computer Interaction
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Hierarchical multi-agent reinforcement learning

Shaping multi-agent systems with gradient reinforcement learning

Continuous strategy replicator dynamics for multi-agent Q-learning

Multi-agent learning

Multi-agent systems with reinforcement hierarchical neuro-fuzzy models

Cooperative Multi-Agent Learning: The State of the Art

Local strategy learning in networked multi-agent team formation

Automated organization design for multi-agent systems

Multi-goal economic search using dynamic search structures

Cooperative reinforcement learning in topology-based multi-agent systems

Similar Documents

Hierarchical multi-agent reinforcement learning

Shaping multi-agent systems with gradient reinforcement learning

Continuous strategy replicator dynamics for multi-agent Q-learning

Multi-agent learning

Multi-agent systems with reinforcement hierarchical neuro-fuzzy models

Cooperative Multi-Agent Learning: The State of the Art

Local strategy learning in networked multi-agent team formation

Automated organization design for multi-agent systems

Multi-goal economic search using dynamic search structures

Cooperative reinforcement learning in topology-based multi-agent systems