Strategy for Learning Cooperative Behavior with Local Information for Multi-agent Systems
Strategy for Learning Cooperative Behavior with Local Information for Multi-agent Systems
复制标题
多智能体系统的局部信息学习合作行为策略
DOI：
10.1007/978-3-030-03098-8_54
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
K.
中科院分区：
文献类型：
--
作者：
Uwano;F. and Takadama;K.
针对任意数量Agent的协作行为学习问题，提出了一种无需通信的多Agent强化学习方法--基于PMRL的任意数量Agent学习（PLAA）。PLAA避免了Agent花费太多时间到达目的地，并促进了本地多Agent的合作，而无需PMRL作为以前的方法通信。为了保证PLAA的有效性，本文将PLAA与Q-学习，以及两个以前的方法在10种迷宫中的2和3代理。实验结果表明：（a）PLAA是解决2个和3个Agent之间协作问题的最有效方法;（B）PLAA使Agent之间能够在较小的迭代次数内进行协作。
Toward learning cooperative behavior for any number of agents, this paper proposes a multi-agent reinforcement learning method without communication, called PMRL-based Learning for Any number of Agents (PLAA). PLAA prevents from agents reaching the purpose for spending too many times, and to promote the local multi-agent cooperation without communication by PMRL as a previous method. To guarantee the effectiveness of PLAA, this paper compares PLAA with Q-learning, and two previous methods in 10 kinds of the maze for the 2 and 3 agents. From the experimental result, we revealed those things: (a) PLAA is the most effective method for cooperation among 2 and 3 agents; (b) PLAA enable the agents to cooperate with each other in small iterations.