Professor of Computer Science, Faculty Network Member of The Energy Initiative
- Faculty Area:
- Artificial Intelligence
- parr at cs.duke.edu
- D209 LSRC
- (919) 660-6537
- Web page:
Ph.D., University of California, Berkeley, 1998
A.B., (cum laude) Princeton University, 1990
Honors & Awards
IJCAI-JAIR Best Paper Award, "Efficient Solution Algorithms for Factored MDPs," 2007; Alfred P. Sloan Fellowship, 2003; Best Student Paper Award, "Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms," UAI, 2001 with Uri Lerner (student first author).
Reasoning under uncertainty, Markov decision processes, Reinforcement learning, Bayesian networks, and Robotics.
- Linear Feature Encoding for Reinforcement Learning, Zhao Song, Ronald Parr, Xuejun Liao, and Lawrence Carin, Neural Information Processing Systems 2016 (NIPS 2016).
- Efficient PAC-optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates, Jason Pazis and Ronald Parr, Proceedings of the Thirtieth AAAI Conference (AAAI 2016).
- Distance Minimization for Reward Learning from Scored Trajectories, Benjamin Burchfiel, Carlo Tomasi, and Ronald Parr, Proceedings of the Thirtieth AAAI Conference (AAAI 2016).
- Greedy Algorithms for Sparse Reinforcement Learning, Christopher Painter-Wakefield and Ronald Parr, Proceedings of the Twenty-Ningth International Conference on Machine Learning (ICML-2012).
- Generalized Value Functions for Large Action Sets, Jason Pazis and Ronald Parr, Proceedings of the Twenty-Eighth International Conference on Machine Learning (ICML-2011).