Achieving Socially Optimal Outcomes in Multiagent Systems with Reinforcement Social Learning
Publication in refereed journal


Times Cited
Web of Science8WOS source URL (as at 31/05/2020) Click here for the latest count
Altmetrics Information
.

Other information
AbstractIn 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 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.
All Author(s) ListHao JY, Leung HF
Journal nameACM Transactions on Autonomous and Adaptive Systems
Year2013
Month9
Day1
Volume Number8
Issue Number3
PublisherASSOC COMPUTING MACHINERY
ISSN1556-4665
eISSN1556-4703
LanguagesEnglish-United Kingdom
KeywordsAlgorithms; Experimentation; general-sum games; multiagent coordination; Performance; Reinforcement social learning; social optimality
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Computer Science, Theory & Methods; COMPUTER SCIENCE, THEORY & METHODS

Last updated on 2020-01-06 at 02:10