Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiency

• URL: http://arxiv.org/abs/2405.17471v2
• Date: Wed, 29 May 2024 01:36:56 GMT
• Title: Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiency
• Authors: Sheng Yue, Xingyuan Hua, Lili Chen, Ju Ren,
• Abstract summary: Federated Reinforcement Learning (FRL) has garnered increasing attention. In this paper, we introduce a new FRL algorithm, named $texttMFPO$. We prove that by proper selection of momentum parameters and interaction frequency, $texttMFPO$ can achieve $tildemathcalO(H-1Nepsilon-3/2N)$ and $tmathcalO(ilon-1N)$.
• Score: 16.002770483584694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Reinforcement Learning (FRL) has garnered increasing attention recently. However, due to the intrinsic spatio-temporal non-stationarity of data distributions, the current approaches typically suffer from high interaction and communication costs. In this paper, we introduce a new FRL algorithm, named $\texttt{MFPO}$, that utilizes momentum, importance sampling, and additional server-side adjustment to control the shift of stochastic policy gradients and enhance the efficiency of data utilization. We prove that by proper selection of momentum parameters and interaction frequency, $\texttt{MFPO}$ can achieve $\tilde{\mathcal{O}}(H N^{-1}\epsilon^{-3/2})$ and $\tilde{\mathcal{O}}(\epsilon^{-1})$ interaction and communication complexities ($N$ represents the number of agents), where the interaction complexity achieves linear speedup with the number of agents, and the communication complexity aligns the best achievable of existing first-order FL algorithms. Extensive experiments corroborate the substantial performance gains of $\texttt{MFPO}$ over existing methods on a suite of complex and high-dimensional benchmarks.

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