Going Beyond Linear RL: Sample Efficient Neural Function ApproximationDownload PDF

May 21, 2021 (edited Jan 21, 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: beyond eluder dimension, neural net function approximation
  • TL;DR: We show sample efficient algorithms for RL with neural net function approximation, which considerably improves upon what can be attained with linear (or eluder dimension) methods.
  • Abstract: 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.
  • Supplementary Material: pdf
  • Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
21 Replies

Loading