Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost
<|reference_start|>Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost: In this paper, we consider model-free federated reinforcement learning for tabular episodic Markov decision processes. Under the coordination of a central server, multiple agents collaboratively explore the environment and learn an optimal policy without sharing their raw data. Despite recent advances in federated Q-learning algorithms achieving near-linear regret speedup with low communication cost, existing algorithms only attain suboptimal regrets compared to the information bound. We propose a novel model-free federated Q-learning algorithm, termed FedQ-Advantage. Our algorithm leverages reference-advantage decomposition for variance reduction and operates under two distinct mechanisms: synchronization between the agents and the server, and policy update, both triggered by events. We prove that our algorithm not only requires a lower logarithmic communication cost but also achieves an 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.<|reference_end|>
@article{zheng2024federated,
    title={Federated Q-Learning with Reference-Advantage Decomposition: Almost
  Optimal Regret and Logarithmic Communication Cost},
    author={Zhong Zheng, Haochen Zhang, Lingzhou Xue},
    journal={arXiv preprint arXiv:2405.18795},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18795},
    primaryClass={stat.ML cs.LG}
}