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

2023/12/23 by Tian Xia, Jia Liu, Xia, Tian +3
Computer Science · Engineering · #FOS: Mathematics #Fault Detection and Control Systems #Machine Learning and ELM #Neural Networks and Applications #Optimization and Control (math.OC)

paper · pdf · doi:10.48550/arxiv.2312.15312

openalex publication_date 2023/12/23 · openalex created_date 2023/12/29 · openalex updated_date 2026/07/28

Abstract

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.

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