Summary of Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-state Mdps, by Filippo Valdettaro and A. Aldo Faisal
Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs
by Filippo Valdettaro, A. Aldo Faisal
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of quantifying Bayesian uncertainty in offline use cases of finite-state Markov Decision Processes (MDPs) with unknown dynamics. The authors propose a principled method to disentangle epistemic and aleatoric uncertainty, leveraging standard Bayesian reinforcement learning methods and analytical computations. They also develop a stochastic gradient-based approach for solving the problem, which is demonstrated in simple gridworlds and validated through ground-truth evaluations on synthetic MDPs. The authors then apply their method to the AI Clinician problem, recommending treatment for patients in intensive care units, showcasing its potential real-world impact and computational scalability. The paper makes code available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make better decisions when we don’t know everything about a situation. It’s like trying to choose the best treatment for a patient in a hospital, but you’re not sure which one will work best. The authors come up with a new way to figure out what decision is most likely to be good, by combining ideas from computer science and statistics. They test their method on simple examples and show that it works well. Then, they apply it to a real-world problem where doctors need to decide the best treatment for patients in intensive care units. |
Keywords
» Artificial intelligence » Reinforcement learning
