Task Allocation through Vacancy Chains: Effects of Action Selection in Multi-Robot Learning

We present an adaptive multi-robot task allocation algorithm based on vacancy chains, a resource distribution process common in animal and human societies. The algorithm uses individual reinforcement learning of task utilities and relies on the specializing abilities of the members of the group to promote dedicated optimal allocation patterns. We demonstrate through experiments in simulation, that different action selection functions have different effects on this specialization, and as a consequence, on the ability of the vacancy chain algorithm to optimally allocate tasks. In particular we study the difference between the Greedy-ǫ and the Boltzmann Softmax action selection functions. We conclude that using a Boltzmann Softmax function makes the vacancy chain algorithm sensitive to different levels of ability in a group of heterogeneous robots as well as to the effects of the underlying group dynamics such as interference and synergy.