A conceptual modeling of flocking-regulated multi-agent reinforcement learning
Abstract
In this paper, we present a multi-agent reinforcement learning (MARL) framework that leverages the emergent behaviors from swarm intelligence (SI). The essential backbone of our framework is an flocking-regulated cooperative learning paradigm in which the cooperation among learning agents is realized via the self-organizing principles derived from natural interaction of flocking boids. In the proposed MARL, each reinforcement learner learns and evolves in the dynamic environment, and is steered by flocking behavior rules such as cohesion, separation, alignment, fear, etcs. The use of the flocking rules provides distributed sensing and communication content for the cooperation of multiple learning agents in the context of pursuit game. The effectiveness of the MARL framework is studied by its application of the multi-agent pursuit game.
Authors: Caishun Chen, Yaqing Hou, Yew-Soon Ong