Learning Multi-Timescale Abstractions for Hierarchical Combinatorial Planning

• URL: http://arxiv.org/abs/2605.17058v1
• Date: Sat, 16 May 2026 16:04:11 GMT
• Title: Learning Multi-Timescale Abstractions for Hierarchical Combinatorial Planning
• Abstract summary: We introduce a model-based hierarchical framework for sequential decision-making.<n>We learn a subgoal-conditioned budget policy jointly with the world model to support context-aware resource allocation.<n>Across challenging SSCO benchmarks, our method outperforms strong baselines.
• Score: 21.105233947788104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The combination of exponentially large action spaces, stochastic dynamics, and long-horizon decision-making under limited resources makes Sequential Stochastic Combinatorial Optimization (SSCO) particularly challenging for reinforcement learning. Hierarchical Reinforcement Learning (HRL) offers a natural decomposition, but it places the high-level policy in a Semi-Markov Decision Process (SMDP) where actions have variable durations, making it difficult to learn a world model that is suitable for planning. We introduce a model-based hierarchical framework for sequential stochastic combinatorial decision-making that directly addresses this issue. Our method combines a latent-space tree-search planner with an SMDP-aware world model for variable-duration decisions. A multi-timescale objective structures the latent dynamics so that transition magnitudes reflect the effective temporal scales of abstract actions, enabling efficient lookahead under adaptive temporal abstraction. We further learn a subgoal-conditioned budget policy jointly with the world model to support context-aware resource allocation. Across challenging SSCO benchmarks, our method outperforms strong baselines.

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