Published on Tue Aug 18 2020

Hoi-To Wai

This paper presents a finite time convergence analysis for a decentralized Stochastic approximation (SA) scheme. The scheme generalizes several algorithms for decentralized machine learning and multi-agent reinforcement learning.

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This paper presents a finite time convergence analysis for a decentralized
stochastic approximation (SA) scheme. The scheme generalizes several algorithms
for decentralized machine learning and multi-agent reinforcement learning. Our
proof technique involves separating the iterates into their respective
consensual parts and consensus error. The consensus error is bounded in terms
of the stationarity of the consensual part, while the updates of the consensual
part can be analyzed as a perturbed SA scheme. Under the Markovian noise and
time varying communication graph assumptions, the decentralized SA scheme has
an expected convergence rate of