Individual and mass behaviour in large population stochastic wireless power control problems: centralized and Nash equilibrium solutions

By Minyi Huang, Peter E. Caines, Roland P. Malhamé. 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475), 2003

423 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/58f30ef5b30f0c6c4354838813afbf5372b1e111

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Tamer Basar
2. Zhuoran Yang
3. Kaiqing Zhang
4. Minyi Huang
5. Quanyan Zhu
6. Afshin Oroojlooyjadid
7. Davood Hajinezhad

In Electrical and Electronic Engineering

1. Tamer Basar
2. Zhuoran Yang
3. Kaiqing Zhang
4. Minyi Huang
5. Quanyan Zhu

Citations in the corpus

• Linear-Quadratic Mean Field Games (2014, background): “For more inspiring elaboration on the notion of -Nash equilibrium, one can refer to, for example, [40] and [7,8,46].”
• Large-Population LQG Games Involving a Major Player: The Nash Certainty Equivalence Principle (2009, methodology): “In [19, 21, 22, 20], the so-called Nash certainty equivalence (NCE) methodology has been developed, where the key idea is to break the large population game into localized optimal control problems via specifying a consistency relationship between the individual strategies and the aggregate populatio”
• Mean Field Games with a Dominating Player (2014, background): “[10,11] investigated stochastic differential game problems involving infinitely many players under the name “Large Population Stochastic Dynamic Games”; and independently, Lasry and Lions [12,13,14] studied similar problems from the viewpoint of the mean-field theory in physics and termed “Mean-Fiel”
• Risk-sensitive mean field stochastic differential games (2011, background): “…such as anonymous sequential population games Jovanovic and Rosenthal (1988); Bergin and Bernhardt (1992), mean-field (MF) stochastic controls Huang et al. (2003, 2007); Yin et al. (2010), mean-field optimization, mean-field teams Tembine et al. (2009), mean-field stochastic games Weintraub…”
• Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019, methodology): “This mean-field view of multi-agent systems has been approached by the mean-field games (MFGs) model [165, 166, 167, 168, 169], the team model with mean-field sharing [170, 171], and the game model with mean-field actions [172].”
• McKean–Vlasov optimal control: The dynamic programming principle (2019, background): “Though many authors have worked on this equation following these initial papers, there has been a drastic surge of interest in the topic in the past decade, due to the connection that it shares with the so–called mean–field game (MFG for short) theory, introduced independently and simultaneously on ”