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| Residual Value Iteration Algorithm Based on Function Approximation |
| CHEN Jianping1,2,3, HU Wen1,2,3, FU Qiming1,2,3,4 |
1.School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009
2.Jiangsu Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou 215009
3.Suzhou Key Laboratory of Mobile Networking and Applied Technologies, Suzhou University of Science and Technology, Suzhou 215009
4.Symbol Computation and Knowledge Engineer of Ministry of Education, Jilin University, Changchun 130012 |
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Abstract Aiming at the problem of unstable and slow convergence of traditional value iteration algorithm, an improved residual value iteration algorithm based on function approximation is proposed. The traditional value iteration algorithm and the value iteration algorithm with Bellman residual are combined. Weight factors are introduced and new rules are constructed to update value function parameter vector. Theoretically, the new parameter vector can guarantee the convergence of the algorithm and solve the unstable convergence problem in the traditional value iteration algorithm. Moreover, the forgotten factor is introduced to speed up the convergence of the algorithm. The experimental results of Grid World problem show that the proposed algorithm has good performance and robustness.
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Received: 02 November 2016
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| Fund:Supported by National Natural Science Foundation of China(No.61602334,61672371,61502329), Natural Science Foundation of Jiangsu Province(No.BK20140283), Funding of Suzhou Science and Technology(No.SZS201609) |
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Corresponding Authors:
CHEN Jianping(Corresponding author), born in 1963, Ph.D., professor. His research interests include big data and analytics, building energy efficiency and intelligent information processing.)
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About author:: CHEN Jianping(Corresponding author), born in 1963, Ph.D., professor. His research interests include big data and analytics, building energy efficiency and intelligent information processing.)
(HU Wen, born in 1992, master student. Her research interests include reinforcement learning and building energy efficiency.)
(FU Qiming, born in 1985, Ph.D., lectu-rer. His research interests include reinforcement learning, pattern recognition and buil-ding energy efficiency.) |
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2017, Vol. 30 
