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One-Shot Averaging for Distributed TD($\lambda$) Under Markov Sampling
March 15, 2024, 4:41 a.m. | Haoxing Tian, Ioannis Ch. Paschalidis, Alex Olshevsky
cs.LG updates on arXiv.org arxiv.org
Abstract: We consider a distributed setup for reinforcement learning, where each agent has a copy of the same Markov Decision Process but transitions are sampled from the corresponding Markov chain independently by each agent. We show that in this setting, we can achieve a linear speedup for TD($\lambda$), a family of popular methods for policy evaluation, in the sense that $N$ agents can evaluate a policy $N$ times faster provided the target accuracy is small enough. …
abstract agent arxiv copy cs.lg decision distributed lambda linear markov process reinforcement reinforcement learning sampling setup show transitions type
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