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Extreme State Aggregation beyond MDPs

Hutter, Marcus


We consider a Reinforcement Learning setup without any (esp. MDP) assumptions on the environment. State aggregation and more generally feature reinforcement learning is concerned with mapping histories/raw-states to reduced/aggregated states. The idea behind both is that the resulting reduced process (approximately) forms a small stationary finite-state MDP, which can then be efficiently solved or learnt. We considerably generalize existing aggregation results by showing that even if the...[Show more]

CollectionsANU Research Publications
Date published: 2014
Type: Conference paper
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 8776
DOI: 10.1007/978-3-319-11662-4_14
Access Rights: Open Access


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