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Dynamic-depth context tree weighting

Abstract:

Reinforcement learning (RL) in partially observable settings is challenging be- cause the agent’s immediate observations are not Markov. Recently proposed methods can learn variable-order Markov models of the underlying process but have steep memory requirements and are sensitive to aliasing between observa- tion histories due to sensor noise. This paper proposes utile context tree weighting (UCTW), a model-learning method that addresses these limitations. UCTW dy- namically expands a s...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Authors


Messias, JV More by this author
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Department:
St Catherines College
Publisher:
Curran Associates Publisher's website
Volume:
30
Pages:
3329-3338
Publication date:
2018-06-01
Acceptance date:
2017-09-04
ISSN:
1049-5258
Pubs id:
pubs:725790
URN:
uri:3bc624f9-75c3-4310-891e-5335ea6957b9
UUID:
uuid:3bc624f9-75c3-4310-891e-5335ea6957b9
Local pid:
pubs:725790
ISBN:
978-1-5108-6096-4

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