The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Communication in reactive multiagent robotic systems
Autonomous Robots
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Behavioral diversity in learning robot teams
Behavioral diversity in learning robot teams
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Interference as a tool for designing and evaluating multi-robot controllers
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Robot task switching under diminishing returns
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Efficient multi-foraging in swarm robotics
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
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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.