NDLI: Fast learning of approximation policies for coordination in distributed networks

Content Provider	IEEE Xplore Digital Library
Author	Shuping Liu Panangadan, A. Raghavendra, C.S. Madni, A.M.
Copyright Year	2014
Description	Author affiliation: Ming Hsieh Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA (Shuping Liu; Panangadan, A.; Raghavendra, C.S.) \|\| Electr. Eng. Dept., Univ. of California Los Angeles, Los Angeles, CA, USA (Madni, A.M.)
Abstract	This paper presents the Average-Max Reinforcement Learning (AMRL) algorithm that can be used to approximate a global policy of a Markov Decision Process (MDP) as a set of local policies that can be executed in a partially observable environment. The local policies are obtained by reinforcement learning and averaging state-action tables under a stochastic process model. This approach overcomes the scalability problem that arises when a large MDP has to be solved exactly. The approach is motivated by the problem of computing coordination policies for correlated but distributed sensors. We demonstrate the performance of this learning scheme on a simulation of a wireless body sensor network. These results show that the performance of the AMRL algorithm is significantly better than a random policy and is close to the optimal policy that can be obtained from solving a global MDP. The results also show that the AMRL algorithm is scalable to networks represented by large state spaces.
Starting Page	285
Ending Page	290
File Size	1820430
Page Count	6
File Format	PDF
ISBN	9781889335490
DOI	10.1109/WAC.2014.6935892
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	TSI Press
Subject Keyword	Approximation algorithms Indexes Silicon Artificial neural networks Microcontrollers Tin Discrete Intelligent Reinforcement Learning Distributed Sensor Networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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