389 citations · 2k across the 19 of their papers we have counts for
3 papers · 1 filter
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO
Logan Engstrom, Andrew Ilyas, Shibani Santurkar +4
We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms: Proximal Policy Optimization (PPO) and Trust Region Po…
A Closer Look at Deep Policy Gradients
Andrew Ilyas, Logan Engstrom, Shibani Santurkar +4
We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. To this end, we propose a fine-grained analysis of stat…
Training GANs with Optimism
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis +1
We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs. Rece…