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Ronald Parr
Approximate Dynamic Programming
Markov Decision Processes
Probabilistic Models For Robotics
Planning Algorithms
Reinforcement Learning
About
Ronald “Ron” Parr is a Professor of Computer Science at Duke University, where he conducts research on machine learning, reinforcement learning, decision making under uncertainty, and planning algorithms. He earned his Ph.D. in Computer Science from the University of California at Berkeley, and before joining Duke he was a postdoctoral researcher at Stanford. His work focuses on approximate dynamic programming, Markov decision processes, and probabilistic models for complex systems such as robotics and sensing. He has received honors including the NSF CAREER Award, a Sloan Research Fellowship, and was elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).
Research Performance Summary
1981-2025
Active Research Span
First Recorded Paper
What is Search?
Year:
1981
Citations:
12
Venue:
N/A
Latest Recorded Paper
General value discrepancies mitigate partial observability in reinforcement learning
Year:
2025
Citations:
2
Venue:
Finding the Frame Workshop at RLC 2025
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.
Publication Venues and Collaboration
Journal, Conference, and Book Publication Breakdown
Top Coauthors
Parr
Lagoudakis
Koller
Research Impact by Period
Papers
2
Citations
4
Avg. Citations / Paper
2
H-Index
2
Papers
10
Citations
433
Avg. Citations / Paper
43.3
H-Index
8
Papers
10
Citations
332
Avg. Citations / Paper
33.2
H-Index
6
Papers
28
Citations
1657
Avg. Citations / Paper
59.2
H-Index
16
Papers
15
Citations
930
Avg. Citations / Paper
62
H-Index
8
Papers
24
Citations
6466
Avg. Citations / Paper
269.4
H-Index
18
Papers
10
Citations
2334
Avg. Citations / Paper
233.4
H-Index
7
Papers
2
Citations
2
Avg. Citations / Paper
1
H-Index
1
Papers
1
Citations
12
Avg. Citations / Paper
12
H-Index
1
Contact and Professional Links
Detected Research Keywords
Markov Decision Processes
Value Function Approximation
Proceedings Twenty Third
Twenty Third Conference
Uncertainty Artificial Intelligence
Least Squares Policy
Squares Policy Iteration
Simultaneous Localization Mapping
Linear Value Function
Stackelberg Strategies Security