Summary of Incentivized Exploration Of Non-stationary Stochastic Bandits, by Sourav Chakraborty and Lijun Chen
Incentivized Exploration of Non-Stationary Stochastic Bandits
by Sourav Chakraborty, Lijun Chen
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 In this research paper, the authors investigate incentivized exploration in multi-armed bandit (MAB) problems with non-stationary reward distributions. The MAB problem involves selecting one of multiple arms to receive a reward, but the rewards change over time. To encourage exploration and adaptability, the authors propose algorithms that compensate players for exploring different arms and provide biased feedback on the received rewards. They demonstrate that these algorithms achieve sublinear regret and compensation over time in both abruptly-changing and continuously-changing environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to encourage players to try new things in a situation where rewards change frequently. The authors develop special strategies for situations where rewards change suddenly or gradually, and they show that these strategies work well even when people might be biased in their feedback about what works best. |
