3 citations · 7 across the 4 of their papers we have counts for
7 papers
Recursive Reasoning in Minimax Games: A Level Gradient Play Method
Zichu Liu, Lacra Pavel
Despite the success of generative adversarial networks (GANs) in generating visually appealing images, they are notoriously challenging to train. In order to stabilize the learning…
An inexact-penalty method for GNE seeking in games with dynamic agents
Andrew R. Romano, Lacra Pavel
We consider a network of autonomous agents whose outputs are actions in a game with coupled constraints. In such network scenarios, agents seeking to minimize coupled cost function…
On the exact convergence to Nash equilibrium in hypomonotone regimes under full and partial-information
Dian Gadjov, Lacra Pavel
In this paper, we consider distributed Nash equilibrium seeking in monotone and hypomonotone games. We first assume that each player has knowledge of the opponents' decisions and p…
Single-timescale distributed GNE seeking for aggregative games over networks via forward-backward operator splitting
Dian Gadjov, Lacra Pavel
We consider aggregative games with affine coupling constraints, where agents have partial information on the aggregate value and can only communicate with neighbouring agents. We p…
On seeking efficient Pareto optimal points in multi-player minimum cost flow problems with application to transportation systems
Shuvomoy Das Gupta, Lacra Pavel
In this paper, we propose a multi-player extension of the minimum cost flow problem inspired by a transportation problem that arises in modern transportation industry. We associate…
From Game-theoretic Multi-agent Log Linear Learning to Reinforcement Learning
Mohammadhosein Hasanbeig, Lacra Pavel
The main focus of this paper is on enhancement of two types of game-theoretic learning algorithms: log-linear learning and reinforcement learning. The standard analysis of log-line…