Efficient Multi-Policy Evaluation for Reinforcement Learning
Shuze Daniel Liu, Claire Chen, Shangtong Zhang
Abstract
To unbiasedly evaluate multiple target policies, the dominant approach among RL practitioners is to run and evaluate each target policy separately. However, this evaluation method is far from efficient because samples are not shared across policies, and running target policies to evaluate themselves is actually not optimal. In this paper, we address these two weaknesses by designing a tailored behavior policy to reduce the variance of estimators across all target policies. Theoretically, we prove that executing this behavior policy with manyfold fewer samples outperforms on-policy evaluation on every target policy under characterized conditions. Empirically, we show our estimator has a substantially lower variance compared with previous best methods and achieves state-of-the-art performance in a broad range of environments.
BibTeX
@article{Liu_Chen_Zhang_2025, title={Efficient Multi-Policy Evaluation for Reinforcement Learning}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/34086}, DOI={10.1609/aaai.v39i18.34086}, abstractNote={To unbiasedly evaluate multiple target policies, the dominant approach among RL practitioners is to run and evaluate each target policy separately. However, this evaluation method is far from efficient because samples are not shared across policies, and running target policies to evaluate themselves is actually not optimal. In this paper, we address these two weaknesses by designing a tailored behavior policy to reduce the variance of estimators across all target policies. Theoretically, we prove that executing this behavior policy with manyfold fewer samples outperforms on-policy evaluation on every target policy under characterized conditions. Empirically, we show our estimator has a substantially lower variance compared with previous best methods and achieves state-of-the-art performance in a broad range of environments.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Liu, Shuze Daniel and Chen, Claire and Zhang, Shangtong}, year={2025}, month={Apr.}, pages={18951-18959} }