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Feature Markov Decision Processes

Hutter, Marcus


General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). So far it is

CollectionsANU Research Publications
Date published: 2009
Type: Conference paper
Source: Advances in Intelligent Systems Research: Proceedings of the 2nd Conference on Artificial General Intelligence (AGI 2009)
DOI: 10.2991/agi.2009.30


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04_Hutter_Feature_Markov_Decision_2009.pdf55.32 kBAdobe PDF    Request a copy

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