提出新符号方法，让策略学习可扩展且保证正确性。
Learning General Policies From Examples
- 基于采样计划的泛化，用击中集算法实现结构终止
- 支持百万状态、数十万特征，显著优于传统符号方法
- 适合需要可解释性和正确性保障的规划任务
针对组合式规划策略学习方法难以扩展的问题，本文提出一种新的符号学习方法。该方法基于采样计划的泛化，采用击中集算法而非SAT/ASP，确保结构终止与无环性。其可处理包含数百万状态和数十万特征的大规模问题，相比以往方法有显著提升。论文分析了方法的理论性质，并在多个基准测试中验证了其可扩展性，证明其在保持策略可解释性和正确性的前提下具备高效处理大规模问题的能力。
原文摘要 · Abstract (English)
Combinatorial methods for learning general policies that solve large collections of planning problems have been recently developed. One of their strengths, in relation to deep learning approaches, is that the resulting policies can be understood and shown to be correct. A weakness is that the methods do not scale up and learn only from small training instances and feature pools that contain a few hundreds of states and features at most. In this work, we propose a new symbolic method for learning policies based on the generalization of sampled plans that ensures structural termination and hence acyclicity. The proposed learning approach is not based on SAT/ASP, as previous symbolic methods, but on a hitting set algorithm that can effectively handle problems with millions of states, and pools with hundreds of thousands of features. The formal properties of the approach are analyzed, and its scalability is tested on a number of benchmarks.
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