高技术通讯2011，Vol.21Issue(2)：179-184,6.DOI:10.3772/j.issn.1002-0470.2011.02.012
基于矢量量化的强化学习及其在机器人行为学习中的应用
Reinforcement learning based on vector quantization and its application in robot behavior learning
摘要
Abstract
Considering that in the course of reinforcement learning (RL), the too large state space causes the problems of long time leaming and difficulty in the learning algorithm's convergence, the paper proposes the VQRL method, a LookupTable reinforcement learning method based on vector quantization (VQ). The proposed method utilizes the codebook of vector quantization to approximate the continuous state space of reinforcement leaming, which solves the partition state space problem of RL and improves the speed of convergence effectively. And based on the equal distortion theory, it uses a distortion sensitive self-organizing feature map (SOFM) to quantize vectors. Therefore, the favorable generalization performance of state space can be obtained. The proposed method was used for learning the behavior of a reactive robot. The experiments showed the effectiveness of the presented algorithm. It can effectively solve the navigation problems under complicated unknown environments.关键词
强化学习(RL)/矢量量化(VQ)/码书/Q(λ)学习/自组织特征映射Key words
reinforcement leaming (RL)/ vector quantization (VQ)/ codebook/ Q(λ) learning/ self-organizing feature maps引用本文复制引用
段勇,伊婧,张永赫,徐心和..基于矢量量化的强化学习及其在机器人行为学习中的应用[J].高技术通讯,2011,21(2):179-184,6.基金项目
国家青年科学基金(60905054)资助项目. （60905054）
