no code implementations • 8 Jan 2018 • Divya Mahajan, Joon Kyung Kim, Jacob Sacks, Adel Ardalan, Arun Kumar, Hadi Esmaeilzadeh
The data revolution is fueled by advances in machine learning, databases, and hardware design.
2 code implementations • NeurIPS 2018 • Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, J. Zico Kolter
We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces.
no code implementations • 24 Feb 2019 • Nolan Wagener, Ching-An Cheng, Jacob Sacks, Byron Boots
In this paper, we show that there exists a close connection between MPC and online learning, an abstract theoretical framework for analyzing online decision making in the optimization literature.
no code implementations • 5 Dec 2022 • Jacob Sacks, Byron Boots
We show that we can contend with this noise by learning how to update the control distribution more effectively and make better use of the few samples that we have.
no code implementations • 5 Dec 2022 • Jacob Sacks, Byron Boots
This requires us to rely on a number of heuristics for generating samples and updating the distribution and may lead to sub-optimal performance.
1 code implementation • 6 Oct 2023 • Jacob Sacks, Rwik Rana, Kevin Huang, Alex Spitzer, Guanya Shi, Byron Boots
A major challenge in robotics is to design robust policies which enable complex and agile behaviors in the real world.