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arXiv · 2606.17377

Performance-Driven Environment Abstraction with Multi-Timescale Learning

Yue Guan·Dipankar Maity·Panagiotis Tsiotras
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Abstract

We study performance-driven environment abstraction for decision-making in large Markov decision processes. Rather than preserving geometric or topological structure, we seek abstractions that directly optimize decision quality. We model abstraction as a controlled approximation obtained by aggregating the state space and enforcing a shared action distribution within each aggregated state. For a fixed partition, we establish a performance guarantee that separates value-function approximation error from the loss introduced by action sharing. Guided by this analysis, we develop a multi-timescale reinforcement learning framework that jointly adapts the policy and a tree-structured environment abstraction. The resulting algorithm refines and coarsens regions of the state space based on Q-value discrepancies, balancing performance against abstraction size and complexity. Empirical results demonstrate substantial state compression, improved sample efficiency, and faster replanning compared to actor-critic baselines.

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Yue Guan, Dipankar Maity, Panagiotis Tsiotras. 2026-06-16. Performance-Driven Environment Abstraction with Multi-Timescale Learning. https://arxiv.org/abs/2606.17377

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