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On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning
Dilip Arumugam · Mark Ho · Noah Goodman · Benjamin Van Roy
Event URL: https://openreview.net/forum?id=jSQYB2aXOv9 »

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.

Author Information

Dilip Arumugam (Stanford University)
Mark Ho (New York University)
Noah Goodman (Stanford University)
Benjamin Van Roy (Stanford University)

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