June 5, 2024, 4:44 a.m. | Aritra Mitra, George J. Pappas, Hamed Hassani

cs.LG updates on arXiv.org arxiv.org

arXiv:2301.00944v3 Announce Type: replace
Abstract: In large-scale distributed machine learning, recent works have studied the effects of compressing gradients in stochastic optimization to alleviate the communication bottleneck. These works have collectively revealed that stochastic gradient descent (SGD) is robust to structured perturbations such as quantization, sparsification, and delays. Perhaps surprisingly, despite the surge of interest in multi-agent reinforcement learning, almost nothing is known about the analogous question: Are common reinforcement learning (RL) algorithms also robust to similar perturbations? We investigate …

abstract arxiv communication cs.ai cs.lg cs.sy difference distributed eess.sy effects error feedback gradient machine machine learning math.oc optimization quantization reinforcement reinforcement learning replace robust scale stochastic temporal type updates

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