首页|A dynamical neural network approach for distributionally robust chance-constrained Markov decision process

A dynamical neural network approach for distributionally robust chance-constrained Markov decision process

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In this paper,we study the distributionally robust joint chance-constrained Markov decision process.Utilizing the logarithmic transformation technique,we derive its deterministic reformulation with bi-convex terms under the moment-based uncertainty set.To cope with the non-convexity and improve the robustness of the solution,we propose a dynamical neural network approach to solve the reformulated optimization problem.Numerical results on a machine replacement problem demonstrate the efficiency of the proposed dynamical neural network approach when compared with the sequential convex approximation approach.

Markov decision processchance constraintsdistributionally robust optimizationmoment-based ambiguity setdynamical neural network

Tian Xia、Jia Liu、Zhiping Chen

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School of Mathematics and Statistics,Xi'an Jiaotong University,Xi'an 710049,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Key R&D Program of China

11991023123713242022YFA1004000

2024

10.1007/s11425-023-2281-0

中国科学:数学(英文版)
中国科学院

中国科学:数学(英文版)

CSTPCD
影响因子：0.36
ISSN：1674-7283
年,卷(期)：2024.67(6)