DASA: Delay-Adaptive Multi-Agent Stochastic Approximation
<|reference_start|>DASA: Delay-Adaptive Multi-Agent Stochastic Approximation: We consider a setting in which $N$ agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume that the up-link transmissions to the server are subject to asynchronous and potentially unbounded time-varying delays. To mitigate the effect of delays and stragglers while reaping the benefits of distributed computation, we propose \texttt{DASA}, a Delay-Adaptive algorithm for multi-agent Stochastic Approximation. We provide a finite-time analysis of \texttt{DASA} assuming that the agents' stochastic observation processes are independent Markov chains. Significantly advancing existing results, \texttt{DASA} is the first algorithm whose convergence rate depends only on the mixing time $\tau_{mix}$ and on the average delay $\tau_{avg}$ while jointly achieving an $N$-fold convergence speedup under Markovian sampling. Our work is relevant for various SA applications, including multi-agent and distributed temporal difference (TD) learning, Q-learning and stochastic optimization with correlated data.<|reference_end|>
@article{fabbro2024dasa:,
    title={DASA: Delay-Adaptive Multi-Agent Stochastic Approximation},
    author={Nicol`o Dal Fabbro, Arman Adibi, H. Vincent Poor, Sanjeev R.
  Kulkarni, Aritra Mitra and George J. Pappas},
    journal={arXiv preprint arXiv:2403.17247},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17247},
    primaryClass={cs.AI cs.RO cs.SY eess.SY math.OC stat.ML}
}