Modeling Human Exploration Through Resource-Rational Reinforcement Learning

Binz, M; Schulz, E

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Modeling Human Exploration Through Resource-Rational Reinforcement Learning

Binz, M., & Schulz, E. (2023). Modeling Human Exploration Through Resource-Rational Reinforcement Learning. In S. Koyejo, & S. Mohamed (Eds.), Advances in Neural Information Processing Systems 35: 36th Conference on Neural Information Processing Systems (NeurIPS 2022) (pp. 31755-31768). Red Hook, NY, USA: Curran.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-ADFC-5 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-ADFD-4

Genre: Conference Paper

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https://papers.nips.cc/paper_files/paper/2022/file/cde542f47c67907e170a1e1a7b32f6ad-Paper-Conference.pdf (Publisher version) Open Access status unknown

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Creators:
Binz, M¹, Author
Schulz, E¹, Author

Affiliations:
1Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3189356

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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 (defined as the number of bits required to implement the given algorithm). 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 changing the description length in our class of models produces the intended effects: reducing description length captures the behavior of brain-lesioned patients while increasing it mirrors cognitive development during adolescence.

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Dates: Accepted: 2022-10Date issued: 2023-04

Publication Status: Issued

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Title: Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)

Place of Event: New Orleans, LA, USA

Start-/End Date: 2022-11-28 - 2022-12-09

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Title: Advances in Neural Information Processing Systems 35: 36th Conference on Neural Information Processing Systems (NeurIPS 2022)

Source Genre: Proceedings

Creator(s):
Koyejo, S, Editor
Mohamed, S, Editor

Affiliations:
-

Publ. Info: Red Hook, NY, USA : Curran

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 31755 - 31768 Identifier: ISBN: 9781713871088