My current research interests include reinforcement learning and its application to motor control problems. Part of this research aims to reduce the variance in estimating policy gradients by reasoning about an agent’s sensor data. Improving the gradient estimation task allows us to build efficient learning algorithms. These algorithms are used to quickly learn effective controllers for a simulated dart throwing problem and for a simulated quadruped locomotion problem.
I was an active member of Black Graduate Engineering and Science Students (BGESS) when I was in graduate school.
Highly Refereed Publications
- Gregory Lawrence and Stuart Russell. Improving Gradient Estimation by Incorporating Sensor Data. In Proceedings of the Twenty-Fourth International Conference on Uncertainty in Artificial Intelligence, Helsinki, Finland, 2008.
- Gregory Lawrence, Noah Cowan, and Stuart Russell. Efficient Gradient Estimation for Motor Control Learning. In Proceedings of the Nineteenth International Conference on Uncertainty in Artificial Intelligence, Acapulco, Mexico, 2003.
- Gregory Lawrence, Aurora Skarra-Gallagher, and Zhichen Xu. Leveraging Social Connections Improves the Performance of a Recommendation System for Yahoo! Web Applications. In Yahoo! Tech Pulse, Santa Clara, CA, 2010.
- Mark A. Paskin and Gregory Lawrence. Junction Tree Algorithms for Solving Sparse Linear Systems. Technical Report UCB/CSD-03-1271, University of California, Berkeley, 2003.
- Gregory Lawrence. Efficient Motor Control Learning. Ph.D. dissertation, University of California at Berkeley, 2009.
- Gregory Lawrence. Improving Gradient Estimation by Incorporating Sensor Data. NIPS Workshop on Robotics Challenges for Machine Learning, Whistler, B.C., Canada, 2007.
- Panel Member, “Defining and Sustaining Quality Mentoring.” Richard Tapia Conference, 2003.