33 citations · 34 across the 2 of their papers we have counts for
3 papers
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
Shixiang Shane Gu, Manfred Diaz, Daniel C. Freeman +7
The goal of continuous control is to synthesize desired behaviors. In reinforcement learning (RL)-driven approaches, this is often accomplished through careful task reward engineer…
Active Domain Randomization
Bhairav Mehta, Manfred Diaz, Florian Golemo +2
Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unknown or cannot easily be used for training…
The AI Driving Olympics at NeurIPS 2018
Julian Zilly, Jacopo Tani, Breandan Considine +14
Despite recent breakthroughs, the ability of deep learning and reinforcement learning to outperform traditional approaches to control physically embodied robotic agents remains lar…