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

• URL: http://arxiv.org/abs/2210.16877v1
• Date: Sun, 30 Oct 2022 16:39:40 GMT
• Title: On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning
• Authors: Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy
• Abstract summary: Decision-making agents in the real world do so under limited information-processing capabilities and without access to cognitive or computational resources. We present a brief survey of information-theoretic models of capacity-limited decision making in biological and artificial agents.
• Score: 43.19983737333797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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