arXiv:2605.12410v2 Announce Type: replace-cross Abstract: We propose and analyze a model-based bootstrap for transition kernels in finite controlled Markov chains (CMCs) with possibly nonstationary or history-dependent control policies, a setting that arises naturally in offline reinforcement learning (RL) when the behavior policy generating the data is unknown. We establish distributional consistency of the bootstrap transition estimator in both a single long-chain regime and the episodic offline RL regime. The key technical tools are a novel bootstrap law of large numbers (LLN) for the visit
The continuous development in offline reinforcement learning necessitates robust methods for policy evaluation in real-world applications.
Improved model-based bootstrap techniques for controlled Markov chains will enhance the reliability and application scope of AI systems, particularly in offline reinforcement learning settings where data generation policies are unknown.
This research provides a more robust statistical framework for evaluating and understanding AI model performance in complex, data-driven environments, potentially accelerating development.
- · AI developers
- · Reinforcement learning researchers
- · Sectors using offline RL for control
- · Traditional statistical methods in dynamic control
More reliable deployment of AI agents in scenarios where data is collected without full knowledge of the generative policy.
Accelerated development and adoption of data-driven AI systems across various industries due to increased confidence in model performance.
Potentially enables new classes of AI applications in sensitive or high-stakes environments where robust performance guarantees are critical.
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Read at arXiv cs.LG