Summary of Approximate Global Convergence Of Independent Learning in Multi-agent Systems, by Ruiyang Jin et al.
Approximate Global Convergence of Independent Learning in Multi-Agent Systems
by Ruiyang Jin, Zaiwei Chen, Yiheng Lin, Jie Song, Adam Wierman
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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 research paper investigates independent learning (IL) in multi-agent systems, aiming to establish global convergence guarantees for scalability. The study focuses on two algorithms, independent Q-learning and independent natural actor-critic, within value-based and policy-based frameworks. The authors provide the first finite-sample analysis for approximate global convergence, showing a sample complexity of O(ε^(-2)) up to an error term capturing dependence among agents. To achieve this result, they develop a novel approach by constructing a separable Markov decision process (MDP) for convergence analysis and bounding the gap due to model difference between the separable MDP and the original one. Theoretical findings are verified through numerical experiments on a synthetic MDP and an electric vehicle charging example. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Independent learning (IL) helps big systems work together better, but it’s hard to prove that everything will work out in the end. This paper looks at two ways IL can be used, called independent Q-learning and natural actor-critic, and shows that they are reliable and efficient. The researchers used a special tool to study how well these methods work, and found that they need a certain number of “tries” to be sure everything will be okay. They tested their ideas on pretend examples to make sure it would work in real life. |
