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H.B. Keller Colloquium

Monday, April 19, 2021
4:00pm to 5:00pm
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Online Event
Improving Policy Learning via Programmatic Domain Knowledge
Yisong Yue, Professor of Computing and Mathematical Sciences, Computing and Mathematical Sciences Dept., California Institute of Technology,

This talk explores how to leverage programmatic domain knowledge to improve policy learning (which includes reinforcement & imitation learning).  I will consider two aspects.  First, how can we express policy classes using domain specific programming languages to yield interesting inductive biases that lead to sample-efficient learning while preserving flexibility and improving interpretability?  Second, building upon the data programming paradigm in supervised learning, how can we use expert-written programs as a form of auxiliary supervision to improve the reliability of policy learning?  I will present problem framings, algorithms, and experiments for two settings: efficient learning of programmatically interpretable policies, and controllable generation of behaviors.

For more information, please contact Diana Bohler by phone at 6262326138 or by email at [email protected].