Inverse Reinforcement Learning for Identifying Cognitive Radars
This talk uses revealed preferences ideas stemming from microeconomics to develop inverse reinforcement learning methods in the context of cognitive radars. The talk comprises three parts. The first part develops inverse reinforcement learning algorithms to identify if a radar is cognitive, and to estimate the radar's utility function and constraints. The second part of the talk discusses how to design a radar that hides is cognition, i.e. how a cognitive radar can act dumb. Finally, we briefly discuss real time inverse reinforcement learning based on stochastic Lagenvin dynamics algorithms.
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Meeting ID: 822 6616 0148
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- Date: 20 Jul 2022
- Time: 11:30 AM to 12:30 PM
- All times are (UTC-04:00) Eastern Time (US & Canada)
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- Starts 01 July 2022 01:00 PM
- Ends 20 July 2022 12:30 PM
- All times are (UTC-04:00) Eastern Time (US & Canada)
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Speakers
Dr. Vikram Krishnamurthy
Inverse Reinforcement Learning for Identifying Cognitive Radars
Biography:
Dr. Vikram Krishnamurthy is a professor in Electrical & Computer Engineering at Cornell. His research interests include statistical signal processing and stochastic control with applications in social networks and adaptive sensing. He is an IEEE fellow, served as distinguished lecturer for the IEEE Signal Processing society, and editor in chief of IEEE Journal Selected Topics in Signal Processing. He is author of the book Partially Observed Markov Decision Processes published by Cambridge University Press.