多Agent系统的Q值强化学习算法

Q-learning Algorithm in Multi-Agent Systems

对多Agent系统的Q值强化学习算法进行研究,将历史信息因素的影响添加到Q值学习中,提出了一个新的基于多Agent系统的Q值学习算法.该算法在保证多Agent系统利益达到相对最大化的同时,也有效降低了Agent之间的冲突率.最后,通过仿真测试验证了该算法的有效性.

This paper investigated reinforcement learning in multi-Agent systems. By adding the historical information in learning process and updating the Q learning function, a new algorithm in multi-agents environment was proposed. This algorithm guaranteed the maximization of interests and reduced the conflict rate among multiple Agents. Finally, the effectiveness of the algorithm was verified by the simulation.