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.

翻译：所有生物和人工智能体都必须在信息处理能力有限的情况下进行学习和决策。因此，一个普适的适应性行为理论应当能够解释智能体的学习历史、决策与能力限制之间的复杂相互作用。计算机科学领域的最新研究通过融合强化学习、贝叶斯决策理论与率失真理论的思想，开始阐明塑造这些动态过程的原则。这一系列研究提出了有限容量贝叶斯强化学习理论，这是一个用于建模处理约束对学习和行动选择影响的统一规范性框架。本文对这一领域的最新算法与理论成果进行了便于理解的综述，特别关注了如何将这些思想应用于认知与行为科学领域的研究问题。