Bayesian Reinforcement Learning with Limited Cognitive Load
This work addresses the challenge of modeling adaptive behavior under capacity limits, which is relevant for understanding biological and artificial agents, but it is incremental as it primarily reviews and synthesizes existing ideas.
The paper tackles the problem of how agents learn and make decisions under information processing constraints by reviewing a unifying normative framework that integrates reinforcement learning, Bayesian decision-making, and rate-distortion theory. It provides an accessible review of recent algorithms and theoretical results, focusing on applications in cognitive and behavioral sciences.
All biological and artificial agents must learn and make decisions given limits on their ability to process information. As such, a general theory of adaptive behavior should be able to account for the complex interactions between an agent's learning history, decisions, and capacity constraints. Recent work in computer science has begun to clarify the principles that shape these dynamics by bridging ideas from reinforcement learning, Bayesian decision-making, and rate-distortion theory. This body of work provides an account of capacity-limited Bayesian reinforcement learning, a unifying normative framework for modeling the effect of processing constraints on learning and action selection. Here, we provide an accessible review of recent algorithms and theoretical results in this setting, paying special attention to how these ideas can be applied to studying questions in the cognitive and behavioral sciences.