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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


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