Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning

2026-05-18 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

This work introduces two significant improvements to Iterated Width (IW) policies for generalized planning, aiming to learn policies that generalize across classical planning domains. The first improvement is a more efficient holistic encoding of the entire search tree, which allows Relational Graph Neural Networks (R-GNNs) to score all transitions in a single forward pass by representing IW(1)-reachable states through their relational differences. The second is Abstracted IW(1), which enhances scaling during novelty checks by abstracting atoms relationally, replacing arguments with their types. This structural compression shifts novelty search scaling from atoms to objects while preserving subgoal structure. These advancements address limitations in prior IW approaches, particularly their unscalable compute costs and inefficiency with thousands of objects, as seen in the International Planning Competition (IPC) 2023 benchmark. The new policies achieve state-of-the-art performance, outperforming previous methods and the classical planner LAMA.

Key takeaway

For research scientists developing generalized planning agents, this work offers a path to overcome scalability bottlenecks in complex domains. You should consider implementing holistic search tree encoding and Abstracted IW(1) to significantly improve policy performance and efficiency, especially when dealing with large object counts. These techniques enable more effective learning of general policies, surpassing traditional planners like LAMA.

Key insights

Efficient lookahead encoding and relational abstraction significantly improve generalized planning policy performance and scalability.

Principles

• Holistic search tree encoding reduces compute.
• Relational abstraction improves novelty check scaling.
• Abstracting atoms by type preserves subgoal structure.

Method

The method involves a holistic encoding of the search tree for R-GNNs to score transitions in one pass, and Abstracted IW(1) for relational abstraction during novelty checks.

In practice

• Use relational differences for state representation.
• Abstract atoms by type for efficient novelty checks.

Topics

• Generalized Planning
• Iterated Width Policies
• Graph Neural Networks
• Relational GNNs
• Relational Abstraction

Code references

• huangcb01/lookahead-routing

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.