Provable Offline Reinforcement Learning for Structured Cyclic MDPs

2026-02-13 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Kyungbok Lee, Angelica Cristello Sarteau, and Michael R. Kosorok introduce CycleFQI, a novel offline reinforcement learning algorithm designed for structured cyclic Markov Decision Processes (MDPs). This framework addresses multi-step decision problems with heterogeneous stage-specific dynamics, transitions, and discount factors across repeating cycles, such as Type 1 Diabetes management. CycleFQI decomposes the cyclic process into modular, stage-wise sub-problems, using a vector of tailored Q-functions to capture within-stage sequences and inter-stage transitions. This modularity allows for partial control, optimizing some stages while others follow predefined policies. The authors establish finite-sample suboptimality error bounds and global convergence rates under Besov regularity, demonstrating that CycleFQI mitigates the curse of dimensionality compared to monolithic baselines. They also propose a sieve-based method for asymptotic inference of optimal policy values. Experiments on simulated and real-world Type 1 Diabetes datasets validate CycleFQI's effectiveness and flexibility.

Key takeaway

For research scientists developing offline reinforcement learning solutions for real-world cyclic problems like medical interventions or traffic control, CycleFQI offers a statistically efficient and theoretically sound approach. Its modular design allows for flexible policy optimization in heterogeneous environments, providing provable benefits in mitigating the curse of dimensionality. You should consider adopting this framework to improve policy performance and enable robust statistical inference for complex multi-stage decision-making systems.

Key insights

CycleFQI offers a modular, theoretically robust offline RL solution for cyclic MDPs with heterogeneous stages.

Principles

• Decompose complex cyclic processes into stage-wise sub-problems.
• Tailor Q-functions to stage-specific dynamics and transitions.
• Modular design enables partial policy optimization and fixed protocols.

Method

CycleFQI extends Fitted Q-Iteration by iteratively updating stage-specific Q-functions linked via a coupled Bellman system, solving K separate least-squares problems per iteration.

In practice

• Apply CycleFQI to Type 1 Diabetes management for adaptive glucose control.
• Use random forest regressors for Q-function approximation.
• Construct confidence regions for optimal policy values using sieve-based estimation.

Topics

• Offline Reinforcement Learning
• Cyclic MDPs
• Fitted Q-Iteration
• Besov Regularity
• Statistical Policy Value Inference

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.