基于强化学习的未知环境多机器人协作搜集

针对多机器人协作复杂搜集任务中学习空间大,学习速度慢的问题,提出了带共享区的双层强化学习算法。该强化学习算法不仅能够实现低层状态-动作对的学习,而且能够实现高层条件-行为对的学习。高层条件-行为对的学习避免了学习空间的组合爆炸,共享区的应用强化了机器人间协作学习的能力。仿真实验结果说明所提方法加快了学习速度,满足了未知环境下多机器人复杂搜集任务的要求。

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

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 muhirobot complex foraging task in unknown environment.