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Abstract: . 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 rst time evaluate it in a toy POMDP setting. (Update)
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BibTeX entry: (Update)
I. Kwee, M. Hutter, and J. Schmidhuber. Market-based reinforcement learning in partially observable worlds. Proceedings of the International Conference on Arti cial Neural Networks (ICANN-2001. http://citeseer.ist.psu.edu/kwee01marketbased.html More
@article{ hutter:01market,
author = "Ivo Kwee and Marcus Hutter and Juergen Schmidhuber",
title = "Market-Based Reinforcement Learning in Partially Observable Worlds",
number = "IDSIA-10-01",
institution = "Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)",
address = "Manno(Lugano), CH",
month = aug,
year = "2001",
pages = "865--873",
journal = "Proceedings of the International Conference on Artificial Neural Networks (ICANN-2001)",
editor = "Georg Dorffner and Horst Bishof and Kurt Hornik",
publisher = "Springer",
series = "Lecture Notes in Computer Science (LNCS 2130)",
url = "citeseer.ist.psu.edu/kwee01marketbased.html",
url = "http://www.hutter1.de/ai/pmarket.htm",
url2 = "http://arxiv.org/abs/cs.AI/0105025",
ftp = "ftp://ftp.idsia.ch/pub/techrep/IDSIA-10-01.ps.gz",
keywords = "Hayek system; reinforcement learning; partial observable environment",
it in a toy POMDP setting." }
Citations (may not include all citations):281 Machine Learning (context) - Watkins, Dayan - 1992
187 Planning and acting in partially observable stochastic domai.. - Kaelbling, Littman et al. - 1995
113 Learning policies for partially observable environments: Sca.. - Littman, Cassandra et al. - 1995
81 Reinforcement learning algorithm for partially observable Ma.. - Jaakkola, Singh et al. - 1995
59 Overcoming incomplete perception with utile distinction memo.. - McCallum - 1993
41 Reinforcement learning in Markovian and nonMarkovian environ.. - Schmidhuber - 1991
39 Continual Learning in Reinforcement Environments - Ring - 1994
36 Learning to predict by the methods of temporal di erences (context) - Sutton - 1988
33 Shifting inductive bias with success-story algorithm - Schmidhuber, Zhao et al. - 1997
23 Solving POMDPs with Levin search and EIRA - Wiering, Schmidhuber - 1996
21 Adaptive Behavior (context) - Wiering, Schmidhuber - 1998
20 Evolutionary principles in self-referential learning (context) - Schmidhuber - 1987
20 A local learning algorithm for dynamic feedforward and recur.. - Schmidhuber - 1989
17 Properties of the bucket brigade (context) - Holland - 1985
12 ZCS: A zeroth level classier system (context) - Wilson - 1994
[Article contains additional citations not shown here]
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