Summary of Momentum-based Federated Reinforcement Learning with Interaction and Communication Efficiency, by Sheng Yue et al.
Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiency
by Sheng Yue, Xingyuan Hua, Lili Chen, Ju Ren
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a new Federated Reinforcement Learning (FRL) algorithm called MFPO, which addresses the issue of high interaction and communication costs in current FRL approaches. By incorporating momentum, importance sampling, and server-side adjustments, MFPO controls policy gradient shifts and optimizes data utilization efficiency. Theoretical analysis shows that MFPO achieves linear speedup with the number of agents and best-in-class communication complexity. Experimental results confirm substantial performance gains on complex high-dimensional benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Reinforcement Learning is a new way for machines to learn together without sharing their private data. But it’s been tricky because different machines have different information. This paper introduces a new method called MFPO that makes FRL more efficient by controlling how the machines share and use their data. The authors show that this approach can handle large numbers of machines and is better than other methods for sharing knowledge. |
Keywords
* Artificial intelligence * Reinforcement learning
