Cooperative multi-robot foraging based on reinforcement learning in unknown environment

Abstract

To reduce the learning status space of complex foraging task and improve the learning speed,a double-deck hierarchical reinforcement learning with share zone is presented.The arithmetic can perform not only the lower hierarchical of state-action learning but also the higher hierachical of station-behavior learning.The higher hierachical of station-behavior learning can avoid the combination explosion of status space.The use of the share zone reinforces the ability of cooperative learning.Simulation results show that the arithmetic can improve the learning speed of robots and satisfy the time need of multirobot complex foraging task in unknown environment.