7 papers · 1 filter
Mean Field Reinforcement Learning
René Carmona, Mathieu Laurière
This monograph provides an introduction to mean field reinforcement learning through the lens of Markov decision processes arising from large-population stochastic control with mea…
Reconciling Discrete-Time Mixed Policies and Continuous-Time Relaxed Controls in Reinforcement Learning and Stochastic Control
Rene Carmona, Mathieu Lauriere
Reinforcement learning (RL) is currently one of the most prominent methods for optimizing dynamical systems, with breakthrough results across various fields. The framework is based…
A Game-Theoretic Framework for Network Formation in Large Populations
Gokce Dayanikli, Mathieu Lauriere
In this paper, we study a model of network formation in large populations. Each agent can choose the strength of interaction (i.e. connection) with other agents to find a Nash equi…
Cooperation, Competition, and Common Pool Resources in Mean Field Games
Gokce Dayanikli, Mathieu Lauriere
Mean field games (MFGs) have been introduced to study Nash equilibria in very large population of self-interested agents. However, when applied to common pool resource (CPR) games,…
How can the tragedy of the commons be prevented?: Introducing Linear Quadratic Mixed Mean Field Games
Gokce Dayanikli, Mathieu Lauriere
In a regular mean field game (MFG), the agents are assumed to be insignificant, they do not realize their effect on the population level and this may result in a phenomenon coined…
Machine Learning Methods for Large Population Games with Applications in Operations Research
Gokce Dayanikli, Mathieu Lauriere
In this tutorial, we provide an introduction to machine learning methods for finding Nash equilibria in games with large number of agents. These types of problems are important for…