Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of learning-based methods in multi-robot planning holds great promise as it enables us to offload the online computational burden of expensive, yet optimal solvers, to an offline learning procedure. Simply put, the idea is to train a policy to copy an optimal pattern generated by a small-scale system, and then transfer that policy to much larger systems, in the hope that the learned strategy scales, while maintaining near-optimal performance. Yet, a number of issues impede us from leveraging this idea to its full potential. This blue-sky paper elaborates some of the key challenges that remain.
翻译：许多多机器人规划问题都因维度的诅咒而不堪重负,这增加了对大规模问题案例采用解决方案的困难。 在多机器人规划中使用基于学习的方法大有希望,因为这使我们能够将昂贵但最优的解决方案的在线计算负担卸到离线学习程序。 简言之,这一想法是培训一项政策,以复制小规模系统产生的最佳模式,然后将这一政策转移到更大的系统,希望所学的战略尺度能够保持近于最佳的绩效。 然而,若干问题阻碍我们充分利用这一理念。 这份蓝天空文件详细阐述了仍然存在的一些关键挑战。
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