NDLI: Achieving Socially Optimal Outcomes in Multiagent Systems with Reinforcement Social Learning

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Achieving Socially Optimal Outcomes in Multiagent Systems with Reinforcement Social Learning

Content Provider	ACM Digital Library
Author	Hao, Jianye Leung, Ho-Fung
Copyright Year	2013
Abstract	In multiagent systems, social optimality is a desirable goal to achieve in terms of maximizing the global efficiency of the system. We study the problem of coordinating on socially optimal outcomes among a population of agents, in which each agent randomly interacts with another agent from the population each round. Previous work [Hales and Edmonds 2003; Matlock and Sen 2007, 2009] mainly resorts to modifying the interaction protocol from random interaction to tag-based interactions and only focus on the case of symmetric games. Besides, in previous work the agents’ decision making processes are usually based on evolutionary learning, which usually results in high communication cost and high deviation on the coordination rate. To solve these problems, we propose an alternative social learning framework with two major contributions as follows. First, we introduce the $\textit{observation}$ mechanism to reduce the amount of communication required among agents. Second, we propose that the agents’ learning strategies should be based on reinforcement learning technique instead of evolutionary learning. Each agent explicitly keeps the record of its current state in its learning strategy, and learn its optimal policy for each state independently. In this way, the learning performance is much more stable and also it is suitable for both symmetric and asymmetric games. The performance of this social learning framework is extensively evaluated under the testbed of two-player general-sum games comparing with previous work [Hao and Leung 2011; Matlock and Sen 2007]. The influences of different factors on the learning performance of the social learning framework are investigated as well.
Starting Page	1
Ending Page	23
Page Count	23
File Format	PDF
ISSN	15564665
e-ISSN	15564703
DOI	10.1145/2517329
Volume Number	8
Issue Number	3
Journal	ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-09-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Reinforcement social learning General-sum games Multiagent coordination Social optimality
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Software

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