4 citations · 5 across the 5 of their papers we have counts for
6 papers
Regret-Minimizing Double Oracle for Extensive-Form Games
Xiaohang Tang, Le Cong Dinh, Stephen Marcus McAleer +1
By incorporating regret minimization, double oracle methods have demonstrated rapid convergence to Nash Equilibrium (NE) in normal-form games and extensive-form games, through algo…
Achieving Better Regret against Strategic Adversaries
Le Cong Dinh, Tri-Dung Nguyen, Alain Zemkoho +1
We study online learning problems in which the learner has extra knowledge about the adversary's behaviour, i.e., in game-theoretic settings where opponents typically follow some n…
Playing Coopetitive Polymatrix Games with Small Manipulation Cost
Shivakumar Mahesh, Nicholas Bishop, Le Cong Dinh +1
Iterated coopetitive games capture the situation when one must efficiently balance between cooperation and competition with the other agents over time in order to win the game (e.g…
Online Markov Decision Processes with Non-oblivious Strategic Adversary
Le Cong Dinh, David Henry Mguni, Long Tran-Thanh +2
We study a novel setting in Online Markov Decision Processes (OMDPs) where the loss function is chosen by a non-oblivious strategic adversary who follows a no-external regret algor…
Online Double Oracle
Le Cong Dinh, Yaodong Yang, Stephen McAleer +6
Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence. This paper proposes new learnin…
Exploiting No-Regret Algorithms in System Design
Le Cong Dinh, Nick Bishop, Long Tran-Thanh
We investigate a repeated two-player zero-sum game setting where the column player is also a designer of the system, and has full control on the design of the payoff matrix. In add…