计算机应用与软件2011，Vol.28Issue(11)：96-98,107,4.
基于模糊推理的多智能体强化学习
FUZZY INFERENCE BASED MULTI-AGENT REINFORCEMENT LEARNING
摘要
Abstract
Under the background of pricing in electronic markets, a multi-agent reinforcement learning algorithm based on fuzzy inference is proposed. Within Markov stochastic game framework, domain knowledge is initialized into fuzzy rules. Agents choose their actions according to those rules, which are updated by reinforcement learning. By doing so, Domain knowledge is effectively integrated; each domain sample is effectively exploited; more importantly, the learning dimension is greatly reduced. Compassion with former pricing algorithm indicates that FI-MARL improves average pricing profits, both individually and collectively; agents acquire long-term intelligence around either the cooperation or the competition issue.关键词
强化学习(RL)/多智能体系统(MAS)/模糊推理/电子市场Key words
Reinforcement learning (RL) Multi-agent system (MAS) Fuzzy inference Electronic market分类
信息技术与安全科学引用本文复制引用
韩伟,鲁霜..基于模糊推理的多智能体强化学习[J].计算机应用与软件,2011,28(11):96-98,107,4.基金项目
2011中国计算机大会论文.国家自然科学基金项目(70802025) （70802025）
