Summary of Combinatorial Multi-armed Bandits: Arm Selection Via Group Testing, by Arpan Mukherjee et al.
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing
by Arpan Mukherjee, Shashanka Ubaru, Keerthiram Murugesan, Karthikeyan Shanmugam, Ali Tajer
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); 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 A novel algorithm is introduced to address combinatorial multi-armed bandits with semi-bandit feedback and a cardinality constraint on the super-arm size, which has been a long-standing challenge in reinforcement learning. The proposed approach combines group-testing for selecting the super arms and quantized Thompson sampling for parameter estimation. This results in a logarithmic complexity reduction compared to state-of-the-art algorithms that rely on exact oracles, making it a practical solution for problems with a large number of arms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers tackle a difficult problem in machine learning called combinatorial multi-armed bandits. Imagine you have many options and want to choose the best one each time, but you only get limited information about each option. This is similar to how we make decisions every day. The authors propose a new way to solve this problem that is more efficient than existing methods. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
