Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games

31 May 2019

Papers citing "Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games"

23 / 23 papers shown

Title
Learning to Steer Markovian Agents under Model Uncertainty Jiawei Huang Vinzenz Thoma Zebang Shen H. Nax Niao He 43 2 0 14 Jul 2024
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective Muhammad Aneeq uz Zaman Alec Koppel Mathieu Laurière Tamer Basar 39 3 0 17 Mar 2024
Distributed Policy Gradient for Linear Quadratic Networked Control with Limited Communication Range Yuzi Yan Yuan-Chung Shen 34 0 0 05 Mar 2024
Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEs Miroslav Krstic Luke Bhan Yuanyuan Shi 54 28 0 18 Mar 2023
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions Shuang Qiu Xiaohan Wei Jieping Ye Zhaoran Wang Zhuoran Yang OffRL 30 11 0 25 Jul 2022
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems Thinh T. Doan 20 15 0 17 Dec 2021
Independent Learning in Stochastic Games Asuman Ozdaglar M. O. Sayin Kaipeng Zhang 16 22 0 23 Nov 2021
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning Shenao Zhang Li Shen Lei Han Li Shen 24 7 0 30 Aug 2021
Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games B. Hambly Renyuan Xu Huining Yang 18 25 0 27 Jul 2021
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization Ying Wen Hui Chen Yaodong Yang Zheng Tian Minne Li Xu Chen Jun Wang 38 11 0 12 Jun 2021
Gradient play in stochastic games: stationary points, convergence, and sample complexity Runyu Zhang Zhaolin Ren Na Li 26 43 0 01 Jun 2021
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games Chen-Yu Wei Chung-Wei Lee Mengxiao Zhang Haipeng Luo 25 82 0 08 Feb 2021
Sample Efficient Reinforcement Learning with REINFORCE Junzi Zhang Jongho Kim Brendan O'Donoghue Stephen P. Boyd 37 100 0 22 Oct 2020
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity Kaipeng Zhang Sham Kakade Tamer Bacsar Lin F. Yang 47 119 0 15 Jul 2020
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning Lingxiao Wang Zhuoran Yang Zhaoran Wang 27 26 0 21 Jun 2020
On the Impossibility of Global Convergence in Multi-Loss Optimization Alistair Letcher 24 32 0 26 May 2020
Approximate Equilibrium Computation for Discrete-Time Linear-Quadratic Mean-Field Games Muhammad Aneeq uz Zaman Kaipeng Zhang Erik Miehling Tamer Basar 11 16 0 30 Mar 2020
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate Yufeng Zhang Qi Cai Zhuoran Yang Zhaoran Wang 111 12 0 08 Mar 2020
GANs May Have No Nash Equilibria Farzan Farnia Asuman Ozdaglar GAN 28 43 0 21 Feb 2020
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms Kaipeng Zhang Zhuoran Yang Tamer Basar 55 1,181 0 24 Nov 2019
Non-Cooperative Inverse Reinforcement Learning Xiangyuan Zhang Kaipeng Zhang Erik Miehling Tamer Basar 8 22 0 03 Nov 2019
$Policy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence$ Policy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence Kaipeng Zhang Bin Hu Tamer Basar 24 119 0 21 Oct 2019
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies Kaipeng Zhang Alec Koppel Haoqi Zhu Tamer Basar 41 186 0 19 Jun 2019