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  Exploration With a Finite Brain

Binz, M., & Schulz, E. (submitted). Exploration With a Finite Brain.

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https://psyarxiv.com/qx4h5/download (Any fulltext)
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 Creators:
Binz, M1, 2, Author           
Schulz, E1, 2, Author           
Affiliations:
1Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3189356              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Equipping artificial agents with useful exploration mechanisms remains a challenge to this day. Humans, on the other hand, seem to manage the trade-off between exploration and exploitation effortlessly. In the present article, we put forward the hypothesis that they accomplish this by making optimal use of limited computational resources. We study this hypothesis by meta-learning reinforcement learning algorithms that sacrifice performance for a shorter description length. The emerging class of models captures human exploration behavior better than previously considered approaches, such as Boltzmann exploration, upper confidence bound algorithms, and Thompson sampling. We additionally demonstrate that changes in description length produce the intended effects: reducing description length captures the behavior of brain-lesioned patients while increasing it echoes cognitive development during adolescence.

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 Dates: 2022-01
 Publication Status: Submitted
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.31234/osf.io/qx4h5
 Degree: -

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