Reinforcement learning in multi-dimensional state-action space using random rectangular coarse coding and gibbs sampling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper presents a coarse coding technique and an action selection scheme for reinforcement learning (RL) in multi-dimensional and continuous state-action spaces following conventional and sound RL manners. RL in high-dimensional continuous domains includes two issues: One is a generalization problem for value-function approximation, and the other is a sampling problem for action selection over multi-dimensional continuous action spaces. The proposed method combines random rectangular coarse coding with an action selection scheme using Gibbs-sampling. The random rectangular coarse coding is very simple and quite suited both to approximate Q-functions in high-dimensional spaces and to execute Gibbs sampling. Gibbs sampling enables us to execute action selection following Boltsmann distribution over high-dimensional action space.

Original languageEnglish
Title of host publicationSICE Annual Conference, SICE 2007
Pages2754-2761
Number of pages8
DOIs
Publication statusPublished - 2007
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
Duration: Sep 17 2007Sep 20 2007

Publication series

NameProceedings of the SICE Annual Conference

Other

OtherSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
Country/TerritoryJapan
CityTakamatsu
Period9/17/079/20/07

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Reinforcement learning in multi-dimensional state-action space using random rectangular coarse coding and gibbs sampling'. Together they form a unique fingerprint.

Cite this