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Value approximation using deep neural networks is at the heart of off-policydeep reinforcement learning, and is often the primary module that provideslearning signals to the rest of the algorithm. While multi-layer perceptronnetworks are universal function approximators, recent works in neural kernelregression suggest the presence of a spectral bias, where fittinghigh-frequency components of the value function requires exponentially moregradient update steps than the low-frequency ones. In this work, we re-examineoff-policy reinforcement learning through the lens of kernel regression andpropose to overcome such bias via a composite neural tangent kernel. With justa single line-change, our approach, the Fourier feature networks (FFN) producestate-of-the-art performance on challenging continuous control domains withonly a fraction of the compute. Faster convergence and better off-policystability also make it possible to remove the target network without sufferingcatastrophic divergences, which further reduces TD}(0)'s estimation bias on afew tasks.

Overcoming the Spectral Bias of Neural Value Approximation