NDLI: Continuous-time Markov decision process with average reward: Using reinforcement learning method

Content Provider	IEEE Xplore Digital Library
Author	Shengde Jia Lincheng Shen Hongtao Xue
Copyright Year	2015
Description	Author affiliation: Coll. of Mechantronic Eng. & Autom., Nat. Univ. of Defense Technol., Changsha, China (Shengde Jia; Lincheng Shen; Hongtao Xue)
Abstract	Markov decision process (MDP) is a foundational framework of reinforcement learning advanced in sequential decision problems. Continuous-time Markov decision process (CTMDP) extends the discrete time MDP model by allowing actions to take place at any time. Prior work has little consideration on the reinforcement learning methods for solving CTMDPs. The aim of our article was to present a reinforcement learning approach based on the path of samples. For the key concept of performance potential function, a policy iteration algorithm with average reward was presented. Then, through the Robbins-Monro method, a temporal difference formula for evaluating the performance potential function was also proposed. Simulation results indicated that the presented algorithms could converge to the solution of the CTMDP problem at a proper speed.
Starting Page	3097
Ending Page	3100
File Size	312437
Page Count	4
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7260117
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Poisson equations Continuous-time Markov Decision Process Process control Learning (artificial intelligence) Markov processes Reinforcement Learning Random variables Steady-state Mathematical model performance potential function
Content Type	Text
Resource Type	Article

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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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