- Safe Model|Based Multi|Agent Mean|Field Reinforcement Learning🔍
- Learning Dual Mean Field Games on Graphs🔍
- Learning Mean|Field Games with Discounted and Average Costs🔍
- Physics|Informed Graph Neural Operator for Mean Field Games on ...🔍
- Mean|field game theory🔍
- Transition|Informed Reinforcement Learning for Large|Scale ...🔍
- Mean Field Games Flock! The Reinforcement Learning Way🔍
- Convergence Analysis of Machine Learning Algorithms for the ...🔍
Scalable Learning for Spatiotemporal Mean Field Games Using ...
Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning
Reinforcement. Learning for Mean Field Games, with Applications to Economics. ... Simple and scalable predictive uncertainty estimation using ...
Learning Dual Mean Field Games on Graphs - IOS Press Ebooks
At mean field equilibria (MFE), an agent's optimal strategy coincides with the population density, characterized by two coupled partial differential equations ( ...
Learning Mean-Field Games with Discounted and Average Costs
In this paper, we develop a learning algorithm that guarantees convergence in a discrete- time stationary mean-field game with nonlinear stochastic state ...
Physics-Informed Graph Neural Operator for Mean Field Games on ...
Our contributions include: (1) We propose a scalable learning framework leveraging. PIGNO to solve G-MFGs with various initial population states ...
Mean-field game theory - Wikipedia
Mean-field game theory is the study of strategic decision making by small interacting agents in very large populations. It lies at the intersection of game ...
Transition-Informed Reinforcement Learning for Large-Scale ...
In contrast, Stackelberg mean-field game (SMFG) provides a powerful tool to model the scenar- ios with enormous homogeneous self-interested followers. SMFGs are ...
Mean Field Games Flock! The Reinforcement Learning Way - IJCAI
Our algorithm finds a Nash Equilibrium and the agents adapt their velocity to match the neighboring flock's average one. We use Fictitious. Play and alternate: ...
Convergence Analysis of Machine Learning Algorithms for the ...
J.-D. Benamou, G. Carlier, and F. Santambrogio, Variational mean field games, in Active Particles. Vol. 1. Advances in Theory ...
LEARNING DEEP MEAN FIELD GAMES FOR MODEL
between competing technologies with economy of scale (Lachapelle et al., 2010). Representing agents as a distribution means that MFG is scalable to ...
Learning While Playing in Mean-Field Games - NSF PAR
Despite the nonsta- tionarity induced by such an alternating scheme, we prove that the proposed algorithm converges to the Nash equilibrium with an explicit ...
Reinforcement Learning for Mean-Field Game
Reinforcement Learning for Mean-Field Game ... Stochastic games provide a framework for interactions among multiple agents and enable a myriad of applications. In ...
Learning Cooperative Mean Field Games on Sparse Chung-Lu ...
... with coefficients above two. Besides a theoretical analysis, we design scalable learning algorithms which especially apply to the ...
Reinforcement Learning for Mean Field Games and ... - eScholarship
In this manuscript, we develop reinforcement learning theory and algorithms for differential games with large number of homogenous players.
Learning Equilibria in Mean-Field Games: Introducing ... - IFAAMAS
These equilibria are also easier to learn than Nash equilibria in N-player games, and can be straight- forwardly approximated using adversarial no-regret ...
Scalable Deep Reinforcement Learning Algorithms for Mean Field ...
Abstract: Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents.
Q-Learning in Regularized Mean-field Games
We establish a value iteration based learning algorithm to this reg- ularized mean-field game using fitted Q-learning. The regularization term in general makes.
Markov game for CV joint adaptive routing in stochastic traffic networks
We develop a scalable homogeneity-based mean-field reinforcement learning algorithm and prove its convergence. •. Our approach can ...
Scalable Learning for Spatiotemporal Mean Field Games Using Physics-Informed Neural Operator. S Liu, X Chen, X Di. Mathematics 12 (6), 803, 2024. 2, 2024.
Exploration Noise for Learning Linear-Quadratic Mean Field Games
The goal of this paper is to demonstrate that common noise may serve as an exploration noise for learning the solution of a mean field game.
Fictitious Play for Mean Field Games: Continuous Time Analysis and ...
Learning in games and MFGs: The scaling limitations of traditional multi-agent learning methods with respect to the number of players remain quite hard to ...