Summary of Sequential Decision Making with Expert Demonstrations Under Unobserved Heterogeneity, by Vahid Balazadeh et al.
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
by Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis
First submitted to arxiv on: 10 Apr 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 proposes a novel approach to online sequential decision-making by leveraging auxiliary demonstrations from experts who made decisions based on unobserved contextual information. This setting is common in applications like self-driving cars, healthcare, and finance, where expert data can be used to inform the learning agent’s decision-making process. The problem is modeled as zero-shot meta-reinforcement learning with an unknown distribution over unobserved contextual variables and a Bayesian regret minimization objective. The proposed Experts-as-Priors algorithm (ExPerior) uses expert data to establish an informative prior distribution, enabling the application of any Bayesian approach for online decision-making. ExPerior is shown to outperform existing behavior cloning, online, and offline baselines for multi-armed bandits, Markov decision processes, and partially observable MDPs, demonstrating its broad utility across different decision-making setups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us learn how machines can make better decisions by using examples from experts who made those decisions based on things they didn’t record. This is important because many real-world problems involve making decisions in complex situations where some information isn’t available. The researchers propose a new way to use this expert data, which lets them make better choices even when they don’t have all the information. They test their approach and show that it works well for different types of decision-making problems. |
Keywords
» Artificial intelligence » Reinforcement learning » Zero shot
