Mean Field Behaviour of Collaborative Multi-Agent Foragers
This work addresses the challenge of undesired dynamical couplings in collaborative multi-agent robotic systems, with implications for biologically-inspired robotics and more general stochastic problems, though it appears incremental in applying mean field methods to a specific domain.
The paper tackles the problem of analyzing collaborative multi-agent foraging by using mean field techniques to reformulate the stochastic system into a deterministic one, enabling computation of limit behaviors and optimality guarantees, and it analyzes performance differences between finite agents and the mean field limit.
Collaborative multi-agent robotic systems where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically-inspired robotics rely on simplifying agents and increasing their number to obtain more efficient solutions to such problems, drawing similarities with natural processes. In this work we focus on the problem of a biologically-inspired multi-agent system solving collaborative foraging. We show how mean field techniques can be used to re-formulate such a stochastic multi-agent problem into a deterministic autonomous system. This de-couples agent dynamics, enabling the computation of limit behaviours and the analysis of optimality guarantees. Furthermore, we analyse how having finite number of agents affects the performance when compared to the mean field limit and we discuss the implications of such limit approximations in this multi-agent system, which have impact on more general collaborative stochastic problems.