1 citations · 1 across the 12 of their papers we have counts for
4 papers · 2 filters
Learning Mean-Field Games through Mean-Field Actor-Critic Flow
Mo Zhou, Haosheng Zhou, Ruimeng Hu
We propose the Mean-Field Actor-Critic (MFAC) flow, a continuous-time learning dynamics for solving mean-field games (MFGs), combining techniques from reinforcement learning and op…
Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent
Ruimeng Hu, Daniel Ralston, Xu Yang +1
We study a continuous-time stochastic Stackelberg game in which a leader seeks to accomplish a primary objective while inferring a hidden parameter of a rational follower. The foll…
Adversarial Decision-Making in Partially Observable Multi-Agent Systems: A Sequential Hypothesis Testing Approach
Haosheng Zhou, Daniel Ralston, Xu Yang +1
Adversarial decision-making in partially observable multi-agent systems requires sophisticated strategies for both deception and counter-deception. This paper presents a sequential…
Integrating Sequential Hypothesis Testing into Adversarial Games: A Sun Zi-Inspired Framework
Haosheng Zhou, Daniel Ralston, Xu Yang +1
This paper investigates the interplay between sequential hypothesis testing (SHT) and adversarial decision-making in partially observable games, focusing on the deceptive strategie…