2 citations · 5 across the 4 of their papers we have counts for
7 papers
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning
Zehao Dou, Jakub Grudzien Kuba, Yaodong Yang
Value function decomposition is becoming a popular rule of thumb for scaling up multi-agent reinforcement learning (MARL) in cooperative games. For such a decomposition rule to hol…
On the One-sided Convergence of Adam-type Algorithms in Non-convex Non-concave Min-max Optimization
Zehao Dou, Yuanzhi Li
Adam-type methods, the extension of adaptive gradient methods, have shown great performance in the training of both supervised and unsupervised machine learning models. In particul…
Gap-Dependent Bounds for Two-Player Markov Games
Zehao Dou, Zhuoran Yang, Zhaoran Wang +1
As one of the most popular methods in the field of reinforcement learning, Q-learning has received increasing attention. Recently, there have been more theoretical works on the reg…
Making Method of Moments Great Again? -- How can GANs learn distributions
Yuanzhi Li, Zehao Dou
Generative Adversarial Networks (GANs) are widely used models to learn complex real-world distributions. In GANs, the training of the generator usually stops when the discriminator…
Finding Mixed Strategy Nash Equilibrium for Continuous Games through Deep Learning
Zehao Dou, Xiang Yan, Dongge Wang +1
Nash equilibrium has long been a desired solution concept in multi-player games, especially for those on continuous strategy spaces, which have attracted a rapidly growing amount o…
Mathematical Analysis of Adversarial Attacks
Zehao Dou, Stanley J. Osher, Bao Wang
In this paper, we analyze efficacy of the fast gradient sign method (FGSM) and the Carlini-Wagner's L2 (CW-L2) attack. We prove that, within a certain regime, the untargeted FGSM c…