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

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.