Multi-armed Bandit Formulation of the Task Partitioning Problem in Swarm Robotics

Abstract

Task partitioning is a way of organizing work consisting in the decomposition of a task into smaller sub-tasks that can be tackled separately. Task partitioning can be beneficial in terms of reduction of physical interference, increase of efficiency, higher parallelism, and exploitation of specialization. However, task partitioning also entails costs in terms of coordination efforts and overheads that can reduce its benefits. It is therefore important to decide when to make use of task partitioning. In this paper we show that such a decision can be formulated as a multi-armed bandit problem. This is advantageous since the theoretical properties of the multi-armed bandit problem are well understood and several algorithms have been proposed for tackling it. We carry out our study in simulation, using a swarm robotics foraging scenario as a testbed. We test an ad-hoc algorithm and two algorithms proposed in the literature for multi-armed bandit problems. The results confirm that the problem of selecting whether to partition a task can be formulated as a multi-armed bandit problem and tackled with existing algorithms.