Using Optimal Foraging Models to Evaluate Learned Robotic Foraging Behavior

Abstract

A key challenge in designing robot teams is determining how to allocate team members to specific roles according to their abilities and the demands of the environment. In this paper we explore this issue in the context of multi-robot foraging, and we show that optimal foraging theory can be used to evaluate our work in learned multi-robot foraging tasks. We present a means by which members of a multi-robot team may use reinforcement learning to allocate themselves to specific foraging roles appropriate to their environment and their abilities. We test this approach in environments with different distributions of various types of attractors and by varying the relative effectiveness of different foraging strategies. We then examine the effectiveness of the algorithm by comparing the distributions learned by the individual robots to those predicted by several optimal foraging models. We show the resulting learned distributions are substantially similar to those predicted by the optimal foraging theory models.