Abstract

Learning algorithms offer a promising method to solve Nash Equilibrium in games. Meanwhile, with the increasing integration of learning algorithms into intelligent and autonomous systems, learning algorithms become agents to play repeated games, leading to the emergence of human-machine games.

In this talk, we will introduce some work of our team: (1) about the optimal strategy of the human in the human-machine games as well as the periodic/quasi-periodic behavior of the system; (2) about a new NE-solving method based on the asymmetric learning dynamics in zero-sum games; (3) about a new NE-solving method based on the non-convergent dynamics of fictitious play algorithm in non-zero-sum games.

Time

2025-06-11  10:00 - 11:00

Speaker

Dr. Yifen Mu, Chinese Academy of Sciences

Room

Room 102