On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning
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.
Cite
@article{arxiv.2210.16877,
title = {On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning},
author = {Dilip Arumugam and Mark K. Ho and Noah D. Goodman and Benjamin Van Roy},
journal= {arXiv preprint arXiv:2210.16877},
year = {2022}
}
Comments
Accepted to the NeurIPS Workshop on Information-Theoretic Principles in Cognitive Systems (InfoCog) 2022. arXiv admin note: text overlap with arXiv:2206.02072