Model-based Bootstrap for Offline Policy Evaluation in Tabular Reinforcement Learning

Research introduces a model-based bootstrap framework designed for uncertainty quantification in offline policy evaluation (OPE) for tabular reinforcement learn

Overview

Research introduces a model-based bootstrap framework designed for uncertainty quantification in offline policy evaluation (OPE) for tabular reinforcement learning. This framework addresses the necessity of reliable assessment of new policies before their deployment in high-stakes reinforcement learning applications, where point estimates alone are considered insufficient. The method aims to unify the tasks of confidence interval generation and variance estimation by estimating the sampling distribution of the evaluation error.

Research Context

Offline policy evaluation (OPE) is identified as a critical component in reinforcement learning scenarios that involve significant risk or impact, necessitating robust policy assessment prior to operational use. Within these settings, the requirement extends beyond simple point estimates of policy performance to include principled uncertainty quantification, such as confidence intervals and variance estimates. This quantification is deemed essential for enabling safe and risk-aware decision-making. Existing OPE approaches, however, are characterized as often suffering from limitations related to robustness, scalability, or finite-sample validity.

Approach

The proposed method employs a model-based bootstrap framework specifically for uncertainty quantification in OPE. This framework is applied within the domain of finite-horizon, time-inhomogeneous Markov decision processes (MDPs). Distinct from classical bootstrap techniques that typically involve resampling entire episodes, this new method operates by regenerating trajectories. These trajectories are generated from an estimated MDP.

This trajectory regeneration mechanism allows the method to accommodate a broader spectrum of offline data formats. These formats include complete trajectories, transition-level observations, and trajectory fragments. The flexibility in handling diverse data types is cited as a factor that further enhances finite-sample statistical efficiency.

Findings

The research establishes several properties for the proposed model-based bootstrap framework:

**Bootstrap Distributional Consistency:** The framework achieves distributional consistency for the bootstrap process.
**Asymptotically Valid Confidence Intervals:** It provides confidence intervals that are asymptotically valid for the target policy value.
**Consistent Variance Estimation:** The method yields consistent variance estimation for the target policy value.

Extensive simulations were conducted to evaluate the method. These simulations indicated that the proposed method accurately captures the sampling distribution of the OPE estimator. Furthermore, in most settings evaluated, the method yielded tighter confidence intervals and more accurate variance estimates compared to other approaches.

Why This Matters

The ability to reliably assess new policies with quantifiable uncertainty is crucial for the safe and risk-aware deployment of reinforcement learning systems in high-stakes applications. The framework's flexibility in handling various offline data formats, including partial or fragmented data, can broaden the applicability of OPE in real-world scenarios where complete, uniform datasets may be unavailable. The established statistical properties, including consistent variance estimation and asymptotically valid confidence intervals, contribute to more robust decision-making based on OPE results.