Summary of Single-loop Federated Actor-critic Across Heterogeneous Environments, by Ye Zhu and Xiaowen Gong
Single-Loop Federated Actor-Critic across Heterogeneous Environments
by Ye Zhu, Xiaowen Gong
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)
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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 The paper explores Federated Reinforcement Learning (FRL) in the context of actor-critic (AC) algorithms. Specifically, it focuses on Single-loop Federated Actor Critic (SFAC), a two-level federated AC algorithm that enables multiple agents to collaborate and learn a shared policy adaptable across heterogeneous environments. The research provides theoretical bounds on the convergence error of SFAC, showing that it asymptotically converges to a near-stationary point with an extent proportional to environment heterogeneity. Numerical experiments using common RL benchmarks demonstrate the effectiveness of SFAC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated reinforcement learning is a new way for computers to learn together and make decisions. The paper looks at how actor-critic algorithms, which are good at solving problems, can work together in different environments. They created a new algorithm called Single-loop Federated Actor Critic that lets agents work together and share their knowledge. The researchers showed that this algorithm works well and gets better as more agents join in. |
Keywords
» Artificial intelligence » Reinforcement learning
