Markov decision processes (MDPs) often suffer from the sensitivity issue under model ambiguity. In recent years, robust MDPs have emerged as an effective framework to overcome this challenge. Distributionally robust MDPs extend the robust MDP framework by incorporating distributional information of the uncertain model parameters to alleviate the conservative nature of robust MDPs.

However, calculating the Laplacian spectrum can be expensive in a matrix base.

To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.

Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance.

It is crucial to quantify the uncertainty of these dynamics models when using them for planning.

Modularity is a critical design principle for both software systems and artificial intelligence (AI).