Summary of The Sample-communication Complexity Trade-off in Federated Q-learning, by Sudeep Salgia and Yuejie Chi
The Sample-Communication Complexity Trade-off in Federated Q-Learning
by Sudeep Salgia, Yuejie Chi
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 The paper investigates the problem of federated Q-learning, where multiple agents aim to collaboratively learn an optimal Q-function for an unknown Markov decision process. The authors explore the trade-off between sample and communication complexities for intermittent communication algorithms. They establish a converse result showing that any algorithm offering speedup in per-agent sample complexity must incur a communication cost proportional to 1/(1-γ). The paper also proposes Fed-DVR-Q, a new federated Q-learning algorithm achieving optimal sample and communication complexities simultaneously. This provides a complete characterization of the trade-off between these complexities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers are trying to figure out how multiple agents can work together to learn the best way to make decisions in an uncertain environment. They’re looking at different ways that agents can share information with each other while still learning quickly and efficiently. The authors found a key limitation on what algorithms can do, showing that any algorithm that helps agents learn faster must also use more communication resources. They also developed a new algorithm that achieves the best possible balance between these two factors. |
