An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - arXiv preprint arXiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
Pettingzoo: Gym for multi-agent reinforcement learning
This paper introduces the PettingZoo library and the accompanying Agent Environment
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …
Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
Learning mean field games: A survey
Non-cooperative and cooperative games with a very large number of players have many
applications but remain generally intractable when the number of players increases …
applications but remain generally intractable when the number of players increases …
A mean-field game approach to cloud resource management with function approximation
Reinforcement learning (RL) has gained increasing popularity for resource management in
cloud services such as serverless computing. As self-interested users compete for shared …
cloud services such as serverless computing. As self-interested users compete for shared …
The Best of Both Worlds in Network Population Games: Reaching Consensus and Convergence to Equilibrium
Reaching consensus and convergence to equilibrium are two major challenges of multi-
agent systems. Although each has attracted significant attention, relatively few studies …
agent systems. Although each has attracted significant attention, relatively few studies …
Beyond centralization: Non-cooperative perimeter control with extended mean-field reinforcement learning in urban road networks
Perimeter control is a traffic management approach aimed at regulating vehicular
accumulation within urban regional networks by managing flows on all border-crossing …
accumulation within urban regional networks by managing flows on all border-crossing …
A general framework for learning mean-field games
This paper presents a general mean-field game (GMFG) framework for simultaneous
learning and decision making in stochastic games with a large population. It first establishes …
learning and decision making in stochastic games with a large population. It first establishes …
A survey on large-population systems and scalable multi-agent reinforcement learning
The analysis and control of large-population systems is of great interest to diverse areas of
research and engineering, ranging from epidemiology over robotic swarms to economics …
research and engineering, ranging from epidemiology over robotic swarms to economics …
Multi-agent dueling Q-learning with mean field and value decomposition
S Ding, W Du, L Ding, L Guo, J Zhang, B An - Pattern Recognition, 2023 - Elsevier
A great deal of multi agent reinforcement learning (MARL) work has investigated how
multiple agents effectively accomplish cooperative tasks utilizing value function …
multiple agents effectively accomplish cooperative tasks utilizing value function …