Summary of Momentum For the Win: Collaborative Federated Reinforcement Learning Across Heterogeneous Environments, by Han Wang et al.
Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments
by Han Wang, Sihong He, Zhili Zhang, Fei Miao, James Anderson
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA); Optimization and Control (math.OC)
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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 presents two novel Federated Reinforcement Learning (FRL) algorithms, FedSVRPG-M and FedHAPG-M, designed for heterogeneous environments. Unlike previous FRL works, which assume similar or identical environments, this problem setup allows agents to operate in arbitrarily diverse settings. The proposed algorithms leverage momentum mechanisms and achieve state-of-the-art convergence results with a sample complexity of O(ε^(-3/2)/N). Notably, the algorithms exhibit linear speedups with respect to the number of agents, emphasizing the benefits of collaboration in finding a common policy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have many robots working together to learn how to solve problems. They don’t share their experiences or data, but they still need to work together to find the best solution. This is a challenging problem called Federated Reinforcement Learning (FRL). In this paper, two new FRL algorithms are proposed that allow robots to work together even when they’re operating in very different environments. These algorithms are special because they can learn quickly and accurately, even with many robots working together. |
Keywords
» Artificial intelligence » Reinforcement learning
