Summary of Federated Q-learning with Reference-advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost, by Zhong Zheng et al.
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost
by Zhong Zheng, Haochen Zhang, Lingzhou Xue
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 A novel model-free federated reinforcement learning algorithm is proposed for tabular episodic Markov decision processes. The algorithm, called FedQ-Advantage, enables multiple agents to collaboratively explore an environment and learn an optimal policy without sharing their raw data. This is achieved through reference-advantage decomposition for variance reduction and two distinct mechanisms: synchronization between agents and a central server, and policy update, both triggered by events. The proposed algorithm requires lower logarithmic communication cost and achieves almost optimal regret, reaching the information bound up to a logarithmic factor and near-linear regret speedup compared to its single-agent counterpart when the time horizon is sufficiently large. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists explore a new way for computers to learn together without sharing all their data. This helps them find the best way to solve problems in complex situations. The algorithm they created, FedQ-Advantage, makes it possible for multiple computers to work together and learn from each other without sharing everything. This is important because it could be used in many areas like medicine or finance where data needs to be protected. |
Keywords
» Artificial intelligence » Reinforcement learning
