论文标题
关于能力限制的认知和强化学习中的利率延伸理论
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning
论文作者
论文摘要
在整个认知科学文献中，都有广泛的协议，即在现实世界中运作的决策代理在有限的信息处理能力下，而无需获得无限的认知或计算资源。先前的工作从这个事实中汲取了灵感，并利用了此类行为或政策的信息理论模型，例如在有界利率约束下运行的通信渠道。同时，一项平行的工作也利用了相同的原则，从速度延伸理论到通过学习目标的概念正式化有能力有限的决策，这促进了贝叶斯遗憾的界限，以实现可证明的学习算法的范围。在本文中，我们旨在通过对这些信息理论模型的简要调查来阐明这一后一种观点，这些模型是生物和人工制剂中的能力受限决策。
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.
