In this article, we develop a framework to obtain graph abstractions for decision-making where the abstractions emerge as a function of the agent's available resources. We discuss the connection of the proposed approach with information-theoretic signal compression and formulate a novel optimization problem to obtain tree-based abstractions that are a function of the agent's computational resources. The structural properties of the new problem are discussed in detail and two algorithmic approaches are proposed. We discuss the quality of, and prove relationships between, the solutions obtained by the two proposed algorithms. The framework is applied to a variety of environments to obtain hierarchical abstractions.

中文翻译：

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Q 树搜索：针对具有计算限制的代理进行分层抽象的信息理论方法

在本文中，我们开发了一个框架来获取用于决策的图抽象，其中抽象是作为代理可用资源的函数出现的。我们讨论了所提出的方法与信息论信号压缩的联系，并制定了一个新的优化问题，以获得作为代理计算资源函数的基于树的抽象。详细讨论了新问题的结构特性，并提出了两种算法方法。我们讨论了由两种建议算法获得的解决方案的质量并证明了它们之间的关系。该框架应用于各种环境以获得分层抽象。