  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.
