NDLI: Socially augmented hierarchical reinforcement learning for reducing complexity in cooperative multi-agent systems

Content Provider	IEEE Xplore Digital Library
Author	Xueqing Sun Ray, L.E. Kralik, J.D. Dongqing Shi
Copyright Year	2010
Description	Author affiliation: Thayer School of Engineering, Dartmouth College, Hanover, NH 03755 USA (Xueqing Sun; Ray, L.E.) \|\| Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH 03755 USA (Kralik, J.D.; Dongqing Shi)
Abstract	This paper addresses the inherent complexity in coordinating learned behavioral strategies of multiple agents working towards a common goal. Because of the interactions among the agents, a primary challenge of policy learning is escalating computational complexity with increasing number of agents and the size of the task space (including action choices and world states). We employ an approach that incorporates social constructs based on analogs from biological systems of high functioning mammals in order to constrain state-action choices in reinforcement learning. Additionally, we use state-space abstraction and a hierarchical learning structure to improve learning efficiency. Theoretical results bound the reduction in computational complexity due to state abstraction, hierarchical learning, and socially-constrained action selection in learning problems that can be described as decentralized Markov decision processes. Simulation results show that these theoretical bounds hold and that satisficing multi-agent coordination policies emerge, which reduce task completion time, computational cost, and memory resources compared to learning with no social knowledge.
Starting Page	3244
Ending Page	3250
File Size	969856
Page Count	7
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5652923
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot kinematics Markov processes Learning Computational complexity Multiagent systems
Content Type	Text
Resource Type	Article

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