Performance-Driven Environment Abstraction with Multi-Timescale Learning
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Computer Science > Machine Learning
Title:Performance-Driven Environment Abstraction with Multi-Timescale Learning
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.
| Subjects: | Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2606.17377 [cs.LG] |
| (or arXiv:2606.17377v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17377
arXiv-issued DOI via DataCite (pending registration)
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