Fitted Occupancy-Ratio Evaluation without Bellman Completeness

Imagine you're trying to evaluate how well a policy performs in a reinforcement learning setting, but you only have past data to work with. This is tricky because the data might not represent the current environment well, leading to inaccurate evaluations. Traditional methods often rely on complex assumptions about the value functions and the data distribution, which can break down when those assumptions don't hold. This is what's called the problem of distribution shift in offline reinforcement learning.

What this paper introduces is a more straightforward approach to tackle this issue. Instead of needing a lot of assumptions about the value functions, it focuses on the occupancy ratio, which is a measure of how often different states are visited under a policy. The new method, called fitted occupancy-ratio evaluation (FORE), uses a fixed-point approach to estimate this ratio directly from the data. It simplifies the process by projecting the data onto a specific class of distributions, making it more robust to the challenges of distribution shift.

The practical takeaway is that this method allows for more reliable policy evaluations without needing to meet stringent completeness conditions. This means that if you're building systems that rely on offline reinforcement learning, you can trust the evaluations more, even when the data doesn't perfectly match the current environment. This could lead to better decision-making in applications like robotics or automated trading, where accurate policy evaluation is crucial.

The paper introduces a new method for evaluating occupancy ratios in offline reinforcement learning, extending existing techniques.

The claims are supported by finite-sample regret bounds and a clear theoretical foundation, though empirical validation could be broader.