Summary of Multi-agent Stochastic Bandits Robust to Adversarial Corruptions, by Fatemeh Ghaffari et al.
Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions
by Fatemeh Ghaffari, Xuchuang Wang, Jinhang Zuo, Mohammad Hajiesmaili
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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 In this paper, researchers tackle a complex problem in multi-agent learning where multiple autonomous agents interact with each other and an environment, but the reward signals they receive are potentially corrupted by an adversary. The goal is to develop an algorithm that can adaptively learn from these corrupted rewards while minimizing the negative impact of the adversary’s actions. To achieve this, the authors propose a novel cooperative learning framework that can resist adversarial corruption and achieve better performance compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how multiple robots or agents can work together to learn from experiences, even when some of their reward signals are fake or manipulated by an opponent. The main idea is to create a system where these agents can share information and adapt to changing situations while protecting themselves from unfair attacks. This helps ensure that the collective performance of all agents improves over time. |
