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 …
Recent developments in machine learning methods for stochastic control and games
R Hu, M Lauriere - arXiv preprint arXiv:2303.10257, 2023 - arxiv.org
Stochastic optimal control and games have a wide range of applications, from finance and
economics to social sciences, robotics, and energy management. Many real-world …
economics to social sciences, robotics, and energy management. Many real-world …
Scalable deep reinforcement learning algorithms for mean field games
Abstract Mean Field Games (MFGs) have been introduced to efficiently approximate games
with very large populations of strategic agents. Recently, the question of learning equilibria …
with very large populations of strategic agents. Recently, the question of learning equilibria …
Approximately solving mean field games via entropy-regularized deep reinforcement learning
The recent mean field game (MFG) formalism facilitates otherwise intractable computation of
approximate Nash equilibria in many-agent settings. In this paper, we consider discrete-time …
approximate Nash equilibria in many-agent settings. In this paper, we consider discrete-time …
[PDF][PDF] Learning mean field games: A survey
M Laurière, S Perrin, M Geist… - arXiv preprint arXiv …, 2022 - researchgate.net
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 …
Policy mirror ascent for efficient and independent learning in mean field games
Mean-field games have been used as a theoretical tool to obtain an approximate Nash
equilibrium for symmetric and anonymous $ N $-player games. However, limiting …
equilibrium for symmetric and anonymous $ N $-player games. However, limiting …
Concave utility reinforcement learning: The mean-field game viewpoint
Concave Utility Reinforcement Learning (CURL) extends RL from linear to concave utilities
in the occupancy measure induced by the agent's policy. This encompasses not only RL but …
in the occupancy measure induced by the agent's policy. This encompasses not only RL but …
Entropy regularization for mean field games with learning
Entropy regularization has been extensively adopted to improve the efficiency, the stability,
and the convergence of algorithms in reinforcement learning. This paper analyzes both …
and the convergence of algorithms in reinforcement learning. This paper analyzes both …
Model-free mean-field reinforcement learning: mean-field MDP and mean-field Q-learning
We study infinite horizon discounted mean field control (MFC) problems with common noise
through the lens of mean field Markov decision processes (MFMDP). We allow the agents to …
through the lens of mean field Markov decision processes (MFMDP). We allow the agents to …
Scaling up mean field games with online mirror descent
We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online
Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash …
Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash …