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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 welldeveloped for small finite state Markov Decision Processes (MDPs). So far it is an art performed by human designers to extract the right state representation out of the bare observations, i.e. to reduce the agent setup to the MDP framework. Before we can think of mechanizing this search for suitable MDPs, we need a...[Show more]

CollectionsANU Research Publications
Date published: 2009-05
Type: Journal article
DOI: 10.2991/agi.2009.30


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