Offline Policy Optimization with Eligible Actions
This addresses offline policy optimization for real-world decision-making applications where online learning is infeasible, offering an incremental improvement over existing methods.
The paper tackles overfitting in offline policy optimization by identifying a phenomenon where learned policies avoid aligned decisions in parts of the state space, and proposes a normalization constraint algorithm that reduces overfitting and improves test performance in healthcare and control tasks.
Offline policy optimization could have a large impact on many real-world decision-making problems, as online learning may be infeasible in many applications. Importance sampling and its variants are a commonly used type of estimator in offline policy evaluation, and such estimators typically do not require assumptions on the properties and representational capabilities of value function or decision process model function classes. In this paper, we identify an important overfitting phenomenon in optimizing the importance weighted return, in which it may be possible for the learned policy to essentially avoid making aligned decisions for part of the initial state space. We propose an algorithm to avoid this overfitting through a new per-state-neighborhood normalization constraint, and provide a theoretical justification of the proposed algorithm. We also show the limitations of previous attempts to this approach. We test our algorithm in a healthcare-inspired simulator, a logged dataset collected from real hospitals and continuous control tasks. These experiments show the proposed method yields less overfitting and better test performance compared to state-of-the-art batch reinforcement learning algorithms.