NDLI: Adaptive multi-robot team reconfiguration using a policy-reuse reinforcement learning approach

Content Provider	ACM Digital Library
Author	Dasgupta, Prithviraj Banerjee, Bikramjit Cheng, Ke
Abstract	We consider the problem of dynamically adjusting the formation and size of robot teams performing distributed area coverage, when they encounter obstacles or occlusions along their path. Based on our earlier formulation of the robotic team formation problem as a coalitional game called a weighted voting game (WVG), we show that the robot team size can be dynamically adapted by adjusting the WVG's quota parameter. We use a Q-learning algorithm to learn the value of the quota parameter and a policy reuse mechanism to adapt the learning process to changes in the underlying environment. Experimental results using simulated e-puck robots within the Webots simulator show that our Q-learning algorithm converges within a finite number of steps in different types of environments. Using the learning algorithm also improves the performance of an area coverage application where multiple robot teams move in formation to explore an initially unknown environment by 5−10%.
Starting Page	330
Ending Page	345
Page Count	16
ISBN	9783642272158
ISSN	03029743
DOI	10.1007/978-3-642-27216-5_23
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-05-02
Publisher Place	Berlin, Heidelberg
Access Restriction	Subscribed
Subject Keyword	Q-learning Coalition game Multi-robot formation
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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Adaptive multi-robot team reconfiguration using a policy-reuse reinforcement learning approach