Convex Functions for Reinforcement Learning

Abstract

We introduce a convex function approximator that has provable convergence in Q-learning, a highly applicable model for reinforcement learning. We present our results on a variety of reinforcement learning tasks, and show promising initial results when compared to naively-built “Input Convex Neural Networks”.

Date
Event
Undergraduate Research Scholar Awards
Location
UT Dallas
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