Firdaus Janoos — accepted AI-conference papers

2020

A Closer Look at Deep Policy Gradients

Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph +1

ICLR 2020talk

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 state-of-the-art methods based on key elements of this framework: gradient estimation, value prediction, and optimization la…

Cited by 98 Source ↗Scholar ↗

2020

Implementation Matters in Deep RL: A Case Study on PPO and TRPO

Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph +1

ICLR 2020talk

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 Policy Optimization (TRPO). Specifically, we investigate the consequences of "code-level optimizations:" algorithm augment…

Cited by 211 Source ↗Scholar ↗