Summary of Adversarial Combinatorial Bandits with Switching Costs, by Yanyan Dong and Vincent Y. F. Tan
Adversarial Combinatorial Bandits with Switching Costs
by Yanyan Dong, Vincent Y. F. Tan
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 tackle the challenge of adversarial combinatorial bandit with a switching cost. They investigate two feedback settings: bandit and semi-bandit. The authors derive lower bounds for the minimax regret in both settings, demonstrating that the problem is more challenging when there are multiple base arms and a time horizon. To achieve these lower bounds, they design algorithms that operate in batches, restricting the number of switches between actions. For the bandit feedback setting, they introduce the Batched-Exp2 algorithm, which achieves a regret upper bound of O((λK)(1/3)T(2/3)I^(4/3)) as T tends to infinity. In the semi-bandit feedback setting, they propose the Batched-BROAD algorithm, which achieves a regret upper bound of O((λK)(1/3)(TI)(2/3)). This work has important implications for the development of efficient algorithms in combinatorial bandits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers study how to make smart choices when faced with many options and uncertain outcomes. They focus on a problem called adversarial combinatorial bandit, where it’s hard to decide which option is best because some options might be worse than others. The authors show that even more difficult versions of this problem can’t do much better than simple algorithms. To achieve this result, they divide the time into smaller chunks and make decisions based on what happened in each chunk. This work has important implications for making good choices when faced with many uncertain options. |
