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Overcoming The Spectral Bias of Neural Value Approximation

ICLR

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Published on

04/29/2022

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ICLR

Value approximation using deep neural networks is at the heart of off-policy deep reinforcement learning, and is often the primary module that provides learning signals to the rest of the algorithm. While multi-layer perceptron networks are universal function approximators, recent works in neural kernel regression suggest the presence of a spectral bias, where fitting high-frequency components of the value function requires exponentially more gradient update steps than the low-frequency ones. In this work, we re-examine off-policy reinforcement learning through the lens of kernel regression and propose to overcome such bias via a composite neural tangent kernel. With just a single line-change, our approach, the Fourier feature networks (FFN) produce state-of-the-art performance on challenging continuous control domains with only a fraction of the compute. Faster convergence and better off-policy stability also make it possible to remove the target network without suffering catastrophic divergences, which further reduces TD(0)’s estimation bias on a few tasks. Code and analysis available at https://geyang.github.io/ffn.

Please cite our work using the BibTeX below.

@inproceedings{
yang2022overcoming,
title={Overcoming The Spectral Bias of Neural Value Approximation},
author={Ge Yang and Anurag Ajay and Pulkit Agrawal},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=vIC-xLFuM6}
}
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