3 citations · 8 across the 10 of their papers we have counts for
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cs.LG2021
Resilient Consensus-based Multi-agent Reinforcement Learning with Function Approximation
Martin Figura, Yixuan Lin, Ji Liu +1
Adversarial attacks during training can strongly influence the performance of multi-agent reinforcement learning algorithms. It is, thus, highly desirable to augment existing algor…
cs.LG2021★ 3 cited
Finite-Time Error Bounds for Distributed Linear Stochastic Approximation
Yixuan Lin, Vijay Gupta, Ji Liu
This paper considers a novel multi-agent linear stochastic approximation algorithm driven by Markovian noise and general consensus-type interaction, in which each agent evolves acc…
cs.LG2019★ 2 cited
A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning
Yixuan Lin, Kaiqing Zhang, Zhuoran Yang +4
This paper considers a distributed reinforcement learning problem in which a network of multiple agents aim to cooperatively maximize the globally averaged return through communica…