Centralized Patrolling with Weakly-Coupled Agents Using Monte Carlo Tree Search

In this paper, a new optimal multi-agent continuous patrol algorithm is proposed to solve the information gathering problem in dynamic environments. First, the environment is modeled as a layout graph with information attached to vertices. Each agent patrols within a specified area and only interacts with its adjacent agents. The problem is then cast as the factored multi-agent partially observable Markov decision process (MPOMDP). Furthermore, a scalable centralized online planning algorithm, called the factored belief-based variable eliminated Monte Carlo planning algorithm, is proposed based on the Monte Carlo tree search (MCTS) method. The proposed algorithm constructs an independent local look-ahead tree for each agent, where actions are coordinated at specific locations of each tree based on the variable elimination algorithm. Finally, we mimic typical patrol problems to empirically evaluate the proposed algorithm by benchmarking it against some state-of-the-art solvers. The results demonstrate that the performance of the proposed algorithm is remarkable for multi-agent systems with the weakly-coupled structure in partially observable scenarios.