经典规划研究者终于有了可扩展的通用策略学习方法——新方法解决了IW策略在大规模问题上的计算瓶颈，做AI规划或强化学习的团队可以直接参考其编码思路。
该研究针对经典规划中的通用策略学习问题，改进了迭代宽度（IW）策略。现有IW方法在评估每个转移时计算成本高且表达能力有限，尤其在对象数量大时效率低下。作者提出两种改进：一是对整个搜索树进行高效整体编码，仅通过状态间的关系差异表示IW(1)可达状态，使关系图神经网络（R-GNN）能单次前向传播评分所有转移；二是定义抽象IW(1)，通过类型抽象原子进行新颖性检查，将缩放从原子数转向对象数。在IPC 2023基准测试和多个领域上，新方法达到了最先进性能，显著超越包括经典规划器LAMA在内的先前工作。
Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning
Generalized planning aims to learn policies that generalize across collections of instances within a classical planning domain. Recent Graph Neural Network (GNN) approaches have learned nearly perfect policies for several domains. This work improves on the recently published idea of Iterated Width (IW) policies. Therein, the policy broadens its successor scope through an IW-lookahead search that can "jump" over multiple transitions, simplifying the problem structure. Yet, each transition is evaluated individually, leading to unscalable compute costs and expressivity limitations. Furthermore, although IW(1) is attractive because it scales linearly with the number of atoms, it becomes inefficient once thousands of objects are considered, as in the International Planning Competition (IPC) 2023 benchmark. We address both limitations. First, we introduce a vastly more efficient holistic encoding of the entire search tree. It jointly represents IW(1)-reachable states only by their relational differences to the current state, enabling Relational GNNs (R-GNNs) to score all transitions in a single forward pass. Second, we define Abstracted IW(1) to improve scaling through relational abstraction during novelty checks. Rather than testing fully instantiated atoms, it abstracts each atom by replacing all but one argument with its type. The original atom is novel if any of its abstracted forms is novel. This structural compression shifts novelty search scaling from atoms to objects, while preserving meaningful subgoal structure. We evaluate our contributions on the hyperscaling IPC 2023 benchmark and across diverse domains, including domains requiring features beyond the $C_2$ logic fragment. Our policies achieve new state-of-the-art performance, significantly surpassing prior work, including the classical planner LAMA.