Summary of Survival Multiarmed Bandits with Bootstrapping Methods, by Peter Veroutis et al.
Survival Multiarmed Bandits with Bootstrapping Methods
by Peter Veroutis, Frédéric Godin
First submitted to arxiv on: 21 Oct 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 This paper tackles the Survival Multiarmed Bandits (S-MAB) problem, where an agent aims to maximize expected cumulative rewards while minimizing the probability of ruin due to budget depletion. The authors propose a framework that balances these competing objectives using a novel objective function and action value estimation approach based on bootstrapping samples from previous rewards. In experiments, their policies outperform benchmarks from the literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves the Survival Multiarmed Bandits (S-MAB) problem, which is an extension of the traditional Multiarmed Bandits (MAB) problem. The goal is to get as many rewards as possible while not spending too much money. To do this, the authors create a special way of balancing these two goals using a new type of math problem and a unique way of learning from previous experiences. This approach helps agents make better decisions that earn more rewards without wasting resources. |
Keywords
» Artificial intelligence » Bootstrapping » Objective function » Probability
