2 citations · 5 across the 13 of their papers we have counts for
13 papers
Extended Graphon Mean-Field Games in Discrete Time
Hamed Amini, Zhongyuan Cao, Gökçe Dayanıklı +3
In this paper, we study games involving a continuum of heterogeneous players in the discrete-time setting with finite state spaces and continuous action spaces. We introduce a new…
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…
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,…
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…
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…