Skip navigation
Skip navigation

Market-based reinforcement learning in partially observable worlds

Kwee, Ivo; Hutter, Marcus; Schmidhuber, Jürgen


Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an agent needs to learn short-term memories of relevant previous events in order to execute optimal actions. Most previous work, however, has focused on reactive settings (MDPs) instead of POMDPs. Here we reimplement a recent approach to market-based RL and for the first time evaluate it in a toy POMDP setting.

CollectionsANU Research Publications
Date published: 2001
Type: Conference paper
Source: Artificial Neural Networks - ICANN 2001 Proceedings
DOI: 10.1007/3-540-44668-0_120


File Description SizeFormat Image
Kwee et al Market based Reinforcement Learning 2001.pdf134 kBAdobe PDFThumbnail

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  20 July 2017/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator