Summary of Federated Offline Reinforcement Learning: Collaborative Single-policy Coverage Suffices, by Jiin Woo et al.
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
by Jiin Woo, Laixi Shi, Gauri Joshi, Yuejie Chi
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA); Machine Learning (stat.ML)
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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 In this paper, researchers explore the benefits of federated learning for offline reinforcement learning (RL) in critical applications where online data collection is impractical or expensive. They design FedLCB-Q, a variant of model-free Q-learning tailored for federated offline RL, which updates local Q-functions at agents and aggregates them at a central server using importance averaging and pessimistic penalty terms. The paper’s sample complexity analysis shows that FedLCB-Q achieves linear speedup with the number of agents without requiring high-quality datasets at individual agents, as long as the local datasets collectively cover the state-action space visited by the optimal policy. This highlights the power of collaboration in federated learning for offline RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can use multiple machines to learn from data together, even if they don’t have all the same information. It’s like trying to solve a puzzle with friends, where each friend has some pieces but not all of them. They designed a new way to do this called FedLCB-Q, which helps the machines work together better. This is important because sometimes we can’t collect data in real-time, so we need ways to learn from what we have already. The researchers showed that their method can make learning faster and more efficient by working with multiple machines at once. |
Keywords
* Artificial intelligence * Federated learning * Reinforcement learning
