Summary of Transfer in Sequential Multi-armed Bandits Via Reward Samples, by Rahul N R and Vaibhav Katewa
Transfer in Sequential Multi-armed Bandits via Reward Samples
by Rahul N R, Vaibhav Katewa
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 presents a novel approach to solving sequential stochastic multi-armed bandit problems, where an agent interacts with multiple arms over multiple episodes. The reward distributions of these arms remain constant within each episode but can change across different episodes. To improve cumulative regret performance, the authors propose an algorithm based on Upper Confidence Bound (UCB) that transfers reward samples from previous episodes. This approach demonstrates significant improvement over standard UCB without transfer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper helps machines learn better by sharing knowledge gained in past experiences to solve similar problems in the future. The idea is simple: when you’ve already played some rounds with different rewards, use that information to make better choices in subsequent rounds. |
