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arXiv · 2210.16877

On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning

Dilip Arumugam·Mark K. Ho·Noah D. Goodman·Benjamin Van Roy
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Abstract

Throughout the cognitive-science literature, there is widespread agreement that decision-making agents operating in the real world do so under limited information-processing capabilities and without access to unbounded cognitive or computational resources. Prior work has drawn inspiration from this fact and leveraged an information-theoretic model of such behaviors or policies as communication channels operating under a bounded rate constraint. Meanwhile, a parallel line of work also capitalizes on the same principles from rate-distortion theory to formalize capacity-limited decision making through the notion of a learning target, which facilitates Bayesian regret bounds for provably-efficient learning algorithms. In this paper, we aim to elucidate this latter perspective by presenting a brief survey of these information-theoretic models of capacity-limited decision making in biological and artificial agents.

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Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy. 2022-10-30. On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning. https://arxiv.org/abs/2210.16877

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