An investigation of belief-free DRL and MCTS for inspection and maintenance planning
This addresses inspection and maintenance planning for deteriorating systems, but it appears incremental as it builds on existing DRL methods with a specific architectural tweak.
The authors tackled inspection and maintenance planning under uncertainty by proposing a belief-free DRL architecture called +RQN, which directly handles erroneous observations, and compared it to Monte Carlo tree search, showing performance through statistical analysis and visualization in belief space.
We propose a novel Deep Reinforcement Learning (DRL) architecture for sequential decision processes under uncertainty, as encountered in inspection and maintenance (I&M) planning. Unlike other DRL algorithms for (I&M) planning, the proposed +RQN architecture dispenses with computing the belief state and directly handles erroneous observations instead. We apply the algorithm to a basic I&M planning problem for a one-component system subject to deterioration. In addition, we investigate the performance of Monte Carlo tree search for the I&M problem and compare it to the +RQN. The comparison includes a statistical analysis of the two methods' resulting policies, as well as their visualization in the belief space.