NDLI: Effect of Virtual Work Braking on Distributed Multi-robot Reinforcement Learning

Content Provider	IEEE Xplore Digital Library
Author	Kawano, H.
Copyright Year	2013
Description	Author affiliation: NTT Commun. Sci. Labs., NTT Corp., Kanagawa, Japan (Kawano, H.)
Abstract	Multi-agent reinforcement learning (MARL) is one of the most promising methods for solving the problem of multi-robot control. One approach for MARL is cooperative Q-learning (CoQ), which uses learning state space containing states and actions of all agents. Inspite of the mathematical foundation for learning convergence, CoQ often suffers from a state space explosion caused by the increase in the number of agents. Another approach to MARL is distributed Q-learning (DiQ), in which each agent uses learning state space not containing the states and actions of other agents. The state space for DiQ can easily be kept compact. Therefore, DiQ seems suitable for solving multi-robot control problems. However, there is no mathematical guarantee for learning convergence in DiQ and it is difficult to apply DiQ to a multi-robot control problem in which definite appointments among working robots must be considered for accomplishing a mission. To solve these problems in applying DiQ for multi-robot control, we treat the work operated by robots as a new agent that regulates robots' motion. We assume that the work has braking ability for its motion. The work stops its motion when the robot attempts to push the work in an inappropriate direction. The policy for the work braking is obtained via dynamic programming of a Markov decision process by using a map of the environment and the work's geometry. By virtue of this, DiQ without joint state space shows convergence. Simulation results also show the high performance of the proposed method in learning speed.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	1987
Ending Page	1994
File Size	702531
Page Count	8
File Format	PDF
ISBN	9781479906529
DOI	10.1109/SMC.2013.341
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-13
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robots Trajectory Aerospace electronics Joints Learning (artificial intelligence) Convergence Space missions distributed system multi-agent learning reinforcement learning robotics embodiment
Content Type	Text
Resource Type	Article

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