Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning

Author:

Zhang Yizhou1ORCID,Qu Guannan2ORCID,Xu Pan3ORCID,Lin Yiheng4ORCID,Chen Zaiwei4ORCID,Wierman Adam4ORCID

Affiliation:

1. Tsinghua University, Beijing, China

2. Carnegie Mellon University, Pittsburgh, PA, USA

3. Duke University, Durham, NC, USA

4. California Institute of Technology, Pasadena, CA, USA

Funder

Amazon AWS

PIMCO Postdoc Fellowship

National Science Foundation

C3 AI Institute

Simoudis Discovery Prize

PIMCO Graduate Fellowship

Publisher

ACM

Reference12 articles.

1. Xin Chen , Guannan Qu , Yujie Tang , Steven Low , and Na Li. 2022. Reinforcement learning for selective key applications in power systems: Recent advances and future challenges . IEEE Transactions on Smart Grid ( 2022 ). Xin Chen, Guannan Qu, Yujie Tang, Steven Low, and Na Li. 2022. Reinforcement learning for selective key applications in power systems: Recent advances and future challenges. IEEE Transactions on Smart Grid (2022).

2. The dynamics of reinforcement learning in cooperative multiagent systems;Claus Caroline;AAAI/IAAI,1998

3. Multi-agent reinforcement learning in stochastic networked systems;Lin Yiheng;Advances in Neural Information Processing Systems,2021

4. Volodymyr Mnih , Koray Kavukcuoglu , David Silver , Andrei A Rusu , Joel Veness , Marc G Bellemare , Alex Graves , Martin Riedmiller , Andreas K Fidjeland , Georg Ostrovski , 2015 . Human-level control through deep reinforcement learning . Nature , Vol. 518 , 7540 (2015), 529. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. 2015. Human-level control through deep reinforcement learning. Nature, Vol. 518, 7540 (2015), 529.

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