Markov Game

Markov games model strategic interactions among multiple agents in dynamic environments, aiming to find equilibrium solutions like Nash equilibria that represent stable outcomes. Current research focuses on developing efficient algorithms for learning these equilibria, particularly in large-scale settings, often employing techniques like mean-field games, actor-critic methods, and policy gradient approaches, and addressing challenges posed by incomplete information, asymmetry, and robustness to uncertainty. This field is crucial for advancing multi-agent reinforcement learning and has significant implications for diverse applications, including robotics, economics, and energy systems.

84papers

Papers - Page 2

May 4, 2024

Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization
Youbang Sun, Tao Liu, P. R. Kumar, Shahin Shahrampour
Nash Equilibrium Markov Game Entropy Regularization Video Game Multi Agent Reinforcement Learning Policy Gradient Linear Convergence

April 30, 2024

Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning
Qiaosheng Zhang, Chenjia Bai, Shuyue Hu, Zhen Wang, Xuelong Li
Information Directed Sampling Sampling Based Algorithm Episodic Reinforcement Learning General Sum Markov Game Multi Agent Reinforcement Learning Markov Game

April 29, 2024

Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman
Markov Game Robust Reinforcement Learning Reinforcement Learning Uncertain Environment Human Face Equilibrium Approximators Multi Agent Reinforcement Learning

March 18, 2024

RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model
Junyi Fan, Yuxuan Han, Jialin Zeng, Jian-Feng Cai, Yang Wang, Yang Xiang, Jiheng Zhang
Action Space Local Client Model Sample Complexity Bound General Sum Markov Game Policy Iteration Markov Game Human Like RL

March 4, 2024

Corruption-Robust Offline Two-Player Zero-Sum Markov Games
Andi Nika, Debmalya Mandal, Adish Singla, Goran Radanović
Nash Equilibrium Policy Zero Sum Markov Game Markov Game

February 20, 2024

Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games
Ninell Oldenburg, Tan Zhi-Xuan
Learning Norm Normative System Probabilistic Rule Markov Game Multi Agent Environment Resource Management LeArning Abstract

February 11, 2024

Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
Yan Dai, Qiwen Cui, Simon S. Du
Multi Agent Single Agent Linear Function Approximation Single Agent RL Markov Game Multi Agent Reinforcement Learning

February 8, 2024

Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
Jiawei Huang, Niao He, Andreas Krause
Mean Field Game Nash Equilibrium Reinforcement Learning Single Agent Reinforcement Learning Mean Field Markov Game

February 2, 2024

February 1, 2024

FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game
Guangzheng Hu, Yuanheng Zhu, Haoran Li, Dongbin Zhao
Markov Game Zero Sum Markov Game Minimax Q Learning Decentralized Minimax

January 10, 2024

Neural Population Learning beyond Symmetric Zero-sum Games
Siqi Liu, Luke Marris, Marc Lanctot, Georgios Piliouras, Joel Z. Leibo, Nicolas Heess
General Sum Game Coarse Correlated Equilibrium Multi Population Markov Game Single Player Graph Building Game

December 30, 2023

Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning
Xin Yu, Rongye Shi, Pu Feng, Yongkai Tian, Simin Li, Shuhao Liao, Wenjun Wu
Cyclic Symmetry Multi Agent Reinforcement Learning Markov Game Approximate Symmetry Passive Symmetry

December 19, 2023

Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property
Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina, Tuomas Sandholm
Optimistic Policy Gradient Markov Game M$ Base Controller Early Stage Convergence Based Potential Game Policy Gradient

December 15, 2023

Assume-Guarantee Reinforcement Learning
Milad Kazemi, Mateo Perez, Fabio Somenzi, Sadegh Soudjani, Ashutosh Trivedi, Alvaro Velasquez
Reinforcement Learning Modular Architecture Markov Game Assume Guarantee

November 15, 2023

On Convex Optimal Value Functions For POSGs
Rafael F. Cunha, Jacopo Castellini, Johan Peralez, Jilles S. Dibangoye
Observable Stochastic Game Multi Agent Planning Markov Game Reinforcement Learning Optimal Value Function

November 1, 2023

Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value
Young Wu, Jeremy McMahan, Yiding Chen, Yudong Chen, Xiaojin Zhu, Qiaomin Xie
Stochastic Policy Nash Equilibrium Markov Game Net Present Value Zero Sum Markov Game

October 30, 2023

Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
Shuang Qiu, Ziyu Dai, Han Zhong, Zhaoran Wang, Zhuoran Yang, Tong Zhang
Posterior Sampling Partial Observation Function Approximation Self Play Markov Game Adversarial Policy

October 14, 2023

Online Parameter Identification of Generalized Non-cooperative Game
Jianguo Chen, Jinlong Lei, Hongsheng Qi, Yiguang Hong
Equilibrium Approximators Markov Game Parameter Identification System Identification

August 17, 2023

Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games
Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang
Sample Efficiency Model Free Algorithm Single Agent Reinforcement Learning Markov Game Model Free Reinforcement Learning Multi Agent Reinforcement Learning Zero Sum Markov Game Episodic Markov Decision Process

Markov Game

Papers - Page 2

Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization

Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning

Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty

RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model

Corruption-Robust Offline Two-Player Zero-Sum Markov Games

Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games

Refined Sample Complexity for Markov Games with Independent Linear Function Approximation

Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL

\widetilde{O}(T^-1) Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games

Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints

FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game

Neural Population Learning beyond Symmetric Zero-sum Games

Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning

Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property

Assume-Guarantee Reinforcement Learning

On Convex Optimal Value Functions For POSGs

Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value

Posterior Sampling for Competitive RL: Function Approximation and Partial Observation

Online Parameter Identification of Generalized Non-cooperative Game

Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games

Markov Game

Papers - Page 2

Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization

Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning

Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty

RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model

Corruption-Robust Offline Two-Player Zero-Sum Markov Games

Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games

Refined Sample Complexity for Markov Games with Independent Linear Function Approximation

Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL

\widetilde{O}(T-1) Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games

Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints

FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game

Neural Population Learning beyond Symmetric Zero-sum Games

Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning

Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property

Assume-Guarantee Reinforcement Learning

On Convex Optimal Value Functions For POSGs

Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value

Posterior Sampling for Competitive RL: Function Approximation and Partial Observation

Online Parameter Identification of Generalized Non-cooperative Game

Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games

\widetilde{O}(T^-1) Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games