Selective Uncertainty Propagation in Offline RL
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912192706641920 |
|---|---|
| author | Krishnamurthy, Sanath Kumar Gangwani, Tanmay Katariya, Sumeet Kveton, Branislav Modi, Shrey Rangi, Anshuka |
| author_facet | Krishnamurthy, Sanath Kumar Gangwani, Tanmay Katariya, Sumeet Kveton, Branislav Modi, Shrey Rangi, Anshuka |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_00284 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Selective Uncertainty Propagation in Offline RL Krishnamurthy, Sanath Kumar Gangwani, Tanmay Katariya, Sumeet Kveton, Branislav Modi, Shrey Rangi, Anshuka Machine Learning Artificial Intelligence 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. |
| title | Selective Uncertainty Propagation in Offline RL |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2302.00284 |