Summary of Pessimistic Causal Reinforcement Learning with Mediators For Confounded Offline Data, by Danyang Wang et al.
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data
by Danyang Wang, Chengchun Shi, Shikai Luo, Will Wei Sun
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed PESsimistic CAusal Learning (PESCAL) algorithm tackles challenges in offline reinforcement learning by leveraging large observational datasets. This novel approach addresses the limitations of unconfoundedness and positivity assumptions, common in randomized experiments. By introducing mediator variables based on the front-door criterion, PESCAL removes confounding bias. Additionally, it incorporates pessimistic principles to address distributional shifts between action distributions and behavior policies. The algorithm learns a lower bound of the mediator distribution function instead of the Q-function, simplifying sequential uncertainty quantification. Theoretical guarantees are provided, with efficacy demonstrated through simulations and real-world experiments utilizing ride-hailing platform datasets. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an innovative way to learn policy from large observational datasets. These datasets are often collected without randomization, which makes it hard to apply existing offline reinforcement learning methods. The new algorithm, PESCAL, tries to fix this issue by introducing “mediator” variables that help remove confounding bias and account for differences between the data and what we want to learn. This approach is tested on ride-hailing platform datasets and shown to be effective. |
Keywords
* Artificial intelligence * Reinforcement learning