Summary of Planning and Learning in Risk-aware Restless Multi-arm Bandit Problem, by Nima Akbarzadeh et al.
Planning and Learning in Risk-Aware Restless Multi-Arm Bandit Problem
by Nima Akbarzadeh, Erick Delage, Yossiri Adulyasak
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 proposed work generalizes the traditional restless multi-arm bandit problem by incorporating risk-awareness, enabling the optimization of limited resources across multiple Markov decision process-based arms. The indexability conditions for this risk-aware objective are established, along with a solution based on Whittle index. To address the learning problem when transition probabilities are unknown, Thompson sampling is proposed and shown to achieve bounded regret that scales sublinearly with episodes and quadratically with arms. Numerical experiments demonstrate the efficacy of the method in reducing risk exposure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper takes a complex problem called restless multi-arm bandits and makes it more realistic by considering risk. Imagine you have many machines (arms) that can make different products, but some are better than others. You want to decide which machine to use, but you don’t know how good each one is yet. The authors create a new way to solve this problem, using an idea called Whittle index. They also show how to learn from experience and avoid making the same mistake twice. |
Keywords
* Artificial intelligence * Optimization
