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Conference Paper

Connecting Exploration, Generalization, and Planning in Correlated Trees

MPS-Authors
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Ludwig,  T
Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Wu,  CM
Institutional Guests, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schulz,  E
Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Ludwig, T., Wu, C., & Schulz, E. (2022). Connecting Exploration, Generalization, and Planning in Correlated Trees. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), 44th Annual Meeting of the Cognitive Science Society (CogSci 2022): Cognitive Diversity (pp. 2940-2946).


Cite as: https://hdl.handle.net/21.11116/0000-000A-0825-4
Abstract
Human reinforcement learning (RL) is characterized by different challenges. Exploration has been studied extensively in multi-armed bandits, while planning has been investigated in multi-step decision tasks. More recent work added structure >to bandits to study generalization. However, most studies focus on a single aspect of learning, making it hard to compare and integrate results. Here, we propose a generative model for constructing Correlated Trees, which provide a unified and scalable method for studying exploration, planning, and generalization in a single task. In an online experiment, we found that, when provided, people use structure to generalize and perform uncertainty-directed exploration, with structure helping more in larger environments. In environments without structure, exploration becomes more random and more planning is needed. All behavioral effects are captured in a single model with recoverable parameters. In conclusion, our results connect past research on human RL in one framework using Correlated Trees.