Summary of On the Convergence Rates Of Federated Q-learning Across Heterogeneous Environments, by Muxing Wang et al.
On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
by Muxing Wang, Pengkun Yang, Lili Su
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This paper explores the role of heterogeneity in the performance of synchronous federated Q-learning, a type of reinforcement learning algorithm designed for large-scale multi-agent systems interacting with diverse environments. The authors investigate how varying parameters such as the number of agents (K) and iterations per average (E) affect the convergence speed. They find that increasing K leads to linear speed-ups in reducing errors due to sampling randomness, similar to homogeneous settings. However, when E is greater than 1, significant performance degradation occurs. The paper provides a detailed characterization of error evolution in heterogeneous environments, showing that the slow convergence of high E values is fundamental rather than an artifact. Experiments demonstrate a two-phase phenomenon, where the error initially decays rapidly before stabilizing. The authors propose using different step sizes for each phase to achieve faster overall convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how different settings affect a type of artificial intelligence called reinforcement learning. It looks at how many “agents” (think of them like robots or computers) are working together and how often they share information with each other. The authors found that when more agents are involved, the algorithm works better. But if the agents share their information too frequently, it actually slows down the process. They also discovered that there is a certain “sweet spot” where the algorithm starts to work faster again. This research can help improve how we use reinforcement learning in real-life applications. |
Keywords
» Artificial intelligence » Reinforcement learning
