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

Tian Xia; Jia Liu; Zhiping Chen

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arXiv · 2312.15312

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

Tian Xia·Jia Liu·Zhiping Chen

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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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BibTeX RIS

Tian Xia, Jia Liu, Zhiping Chen. 2023-12-23. A dynamical neural network approach for distributionally robust chance constrained Markov decision process. https://arxiv.org/abs/2312.15312

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