Weighted methods improve policy evaluation and learning from offline data
Deep Weighted Bellman Residual Minimization for $Q^*$ Estimation
Machine Learning
Summary
Evaluating how good a decision-making strategy is using old data can be tricky because the data may not reflect all situations and can lead to overconfidence in estimates. The paper introduces a new way to combine expert examples with regular data to better estimate the true value of actions. This approach adjusts how much different data points count, improving learning accuracy without relying on common assumptions. The authors also prove their method converges reliably and show through tests that it works better than some existing approaches.
What this means in practice
- •For autonomous vehicle teams: Improve offline evaluation of driving policies by integrating expert driver data with typical driving logs to estimate safer control strategies.
- •For robotics engineers: Use weighted data combining expert and behavioral logs to better estimate quality of robot control policies from pre-recorded experiences.
Authors
Lican Kang, Jerry Zhijian Yang, Cheng Yuan, Chen Zhong
Abstract
Off-policy evaluation is a foundational component of offline reinforcement learning, aiming to assess and optimize policy performance using pre-collected datasets. However, such datasets often suffer from pronounced challenges, including distribution shift, $Q$-value overestimation, and low sample utilization efficiency. To address these issues, this paper introduces a weighted Bellman residual minimization framework that incorporates density ratio weighting by effectively integrating expert demonstrations with behavioral data. The proposed weighting scheme departs from the conventional completeness assumption commonly imposed in the theoretical analysis of deep reinforcement learning. We establish a sharp convergence rate for density ratio estimation and derive the convergence rate for the excess risk of resulting deep $Q^*$ estimator. Extensive empirical evaluations demonstrate that, compared to existing methods, our method achieves significant improvements in numerical performance and policy generalization, providing specific guidance for the rational utilization of expert demonstrations.