Can I read Going Beyond Linear RL: Sample Efficient Neural Function Approximation on EtoBox?

Going Beyond Linear RL: Sample Efficient Neural Function Approximation by Huang, Baihe; Huang, Kaixuan; Kakade, Sham M.; Lee, Jason D.; Lei, Qi; Wang, Runzhe; Yang, Jiaqi is a scholarly article available to read on EtoBox.

What is Going Beyond Linear RL: Sample Efficient Neural Function Approximation about?

Deep Reinforcement Learning (RL) powered by neural net approximation of the Q function has had enormous empirical success. While the theory of RL has traditionally focused on linear function approximation (or eluder dimension) approaches, little is known about nonlinear RL with neural net approximations of the Q functions. This is the focus of this work, where we study function approximation with two-layer neural networks (considering both ReLU and polynomial activation functions). Our first result is a computationally and statistically efficient algorithm in the generative model setting under completeness for two-layer neural networks. Our second result considers this setting but under only realizability of the neural net function class. Here, assuming deterministic dynamics, the sample complexity scales linearly in the algebraic dimension. In all cases, our results significantly improve upon what can be attained with linear (or eluder dimension) methods.

Author

Huang, Baihe; Huang, Kaixuan; Kakade, Sham M.; Lee, Jason D.; Lei, Qi; Wang, Runzhe; Yang, Jiaqi

Published

2021

Language

EN