One-Shot Averaging for Distributed TD($\lambda$) Under Markov Sampling

Haoxing Tian; Ioannis Ch. Paschalidis; Alex Olshevsky

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arXiv · 2403.08896

One-Shot Averaging for Distributed TD($\lambda$) Under Markov Sampling

Haoxing Tian·Ioannis Ch. Paschalidis·Alex Olshevsky

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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. Notably, this speedup is achieved by ``one shot averaging,'' a procedure where the agents run TD($\lambda$) with Markov sampling independently and only average their results after the final step. This significantly reduces the amount of communication required to achieve a linear speedup relative to previous work.

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Haoxing Tian, Ioannis Ch. Paschalidis, Alex Olshevsky. 2024-03-13. One-Shot Averaging for Distributed TD($\lambda$) Under Markov Sampling. https://arxiv.org/abs/2403.08896

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