Evaluating Strategy Exploration in Empirical Game-Theoretic Analysis
Yongzhao Wang*, Qiurui Ma*, Michael Wellman,
Working Paper

In Empirical Game Theoretic Analysis (EGTA), game models are iteratively extended to include the Nash Equilibrium of the underlying true games. The Strategy Exploration process dictates which new strategies to add to the game models next based on current available information. We investigate the methodological considerations in evaluating different strategy exploration processes in EGTA and highlight a consistency criteria that past literatures violate.

Learning a Decision Module by Imitating Driver’s Control Behaviors
Junning Huang*, Sirui Xie*, Jiankai Xun, Qiurui Ma, Chunxiao Liu, Bolei Zhou,
The Conference on Robot Learning (CoRL), 2020
[paper] [project page] [code]

we propose a hybrid framework to learn neural decisions in the classical modular pipeline through end-to-end imitation learning. This hybrid framework can preserve the merits of the classical pipeline such as the strict enforcement of physical and logical constraints while learning complex driving decisions from data.