Selective Uncertainty Propagation in Offline RL
Sanath Kumar Krishnamurthy, Tanmay Gangwani, Sumeet Katariya, Branislav Kveton, Shrey Modi, Anshuka Rangi
Abstract
We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algorithms. To learn this, it is sufficient to evaluate the treatment effect of deviating from the behavioral policy at step h after having optimized the policy for all future steps. Since the policy at any step can affect next-state distributions, the related distributional shift challenges can make this problem far more statistically hard than estimating such treatment effects in the stochastic contextual bandit setting. However, the hardness of many real-world RL instances lies between the two regimes. We develop a flexible and general method called selective uncertainty propagation for confidence interval construction that adapts to the hardness of the associated distribution shift challenges. We show benefits of our approach on toy environments and demonstrate the benefits of these techniques for offline policy learning.
BibTeX
@article{Krishnamurthy_Gangwani_Katariya_Kveton_Modi_Rangi_2025, title={Selective Uncertainty Propagation in Offline RL}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33977}, DOI={10.1609/aaai.v39i17.33977}, abstractNote={We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algorithms. To learn this, it is sufficient to evaluate the treatment effect of deviating from the behavioral policy at step h after having optimized the policy for all future steps. Since the policy at any step can affect next-state distributions, the related distributional shift challenges can make this problem far more statistically hard than estimating such treatment effects in the stochastic contextual bandit setting. However, the hardness of many real-world RL instances lies between the two regimes. We develop a flexible and general method called selective uncertainty propagation for confidence interval construction that adapts to the hardness of the associated distribution shift challenges. We show benefits of our approach on toy environments and demonstrate the benefits of these techniques for offline policy learning.}, number={17}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Krishnamurthy, Sanath Kumar and Gangwani, Tanmay and Katariya, Sumeet and Kveton, Branislav and Modi, Shrey and Rangi, Anshuka}, year={2025}, month={Apr.}, pages={17974-17982} }