Frontier exploration and reinforcement learning have historically been used to solve the problem of enabling many mobile robots to autonomously and cooperatively explore complex surroundings. These methods need to keep an internal global map for navigation, but they do not take into consideration the high costs of communication and information sharing between robots. This study offers CQLite, a novel distributed Q-learning technique designed to minimize data communication overhead between robots while achieving rapid convergence and thorough coverage in multi-robot exploration. The proposed CQLite method uses ad hoc map merging, and selectively shares updated Q-values at recently identified frontiers to significantly reduce communication costs. The theoretical analysis of CQLite's convergence and efficiency, together with extensive numerical verification on simulated indoor maps utilizing several robots, demonstrates the method's novelty. With over 2x reductions in computation and communication alongside improved mapping performance, CQLite outperformed cutting-edge multi-robot exploration techniques like Rapidly Exploring Random Trees and Deep Reinforcement Learning. Related codes are open-sourced at \url{https://github.com/herolab-uga/cqlite}.

翻译：前沿探索和强化学习历来被用于解决多移动机器人自主协作探索复杂环境的问题。这些方法需要维护用于导航的内部全局地图，但未考虑机器人之间通信和信息共享的高昂成本。本研究提出CQLite——一种新颖的分布式Q学习技术，旨在最小化机器人间的数据通信开销，同时实现多机器人探索中的快速收敛和全面覆盖。所提出的CQLite方法采用即席地图合并，并选择性地共享最新识别前沿处的更新Q值，从而显著降低通信成本。对CQLite收敛性和效率的理论分析，以及在多个机器人模拟室内地图上进行的广泛数值验证，证明了该方法的新颖性。与快速探索随机树和深度强化学习等前沿多机器人探索技术相比，CQLite在计算和通信方面实现了超过2倍的降低，同时映射性能得到提升，表现更优。相关代码已开源在\url{https://github.com/herolab-uga/cqlite}。