Summary of Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities, by Hong Xie et al.
Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities
by Hong Xie, Jinyu Mo, Defu Lian, Jie Wang, Enhong Chen
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 new variant of the multi-player multi-armed bandit (MAB) model, which captures stochastic request arrivals and player allocation policies. The challenge is designing a distributed learning algorithm allowing players to select arms without communication. A greedy algorithm is proposed for locating an optimal arm pulling profile with polynomial computational complexity, while an iterative distributed algorithm ensures commitment to the optimal profile in constant rounds. The explore-then-commit (ETC) framework addresses the online setting with unknown model parameters. An exploration strategy is designed for estimating the optimal profile, and an iterative algorithm guarantees consensus on the optimal profile within M rounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way of solving distributed selection problems. Imagine a situation where many players need to make decisions independently without talking to each other. The authors develop a new model that takes into account how requests arrive and how players choose which options to pick. They propose two algorithms: one for quickly finding the best solution, and another for ensuring all players agree on the best choice. This work is important because it helps solve problems that arise in many real-world scenarios where people need to make decisions without coordination. |
