On the Performance of Temporal Difference Learning With Neural Networks

Haoxing Tian; Ioannis Ch. Paschalidis; Alex Olshevsky

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arXiv · 2312.05397

On the Performance of Temporal Difference Learning With Neural Networks

Haoxing Tian·Ioannis Ch. Paschalidis·Alex Olshevsky

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Abstract

Neural Temporal Difference (TD) Learning is an approximate temporal difference method for policy evaluation that uses a neural network for function approximation. Analysis of Neural TD Learning has proven to be challenging. In this paper we provide a convergence analysis of Neural TD Learning with a projection onto $B(\theta_0, \omega)$, a ball of fixed radius $\omega$ around the initial point $\theta_0$. We show an approximation bound of $O(\epsilon) + \tilde{O} (1/\sqrt{m})$ where $\epsilon$ is the approximation quality of the best neural network in $B(\theta_0, \omega)$ and $m$ is the width of all hidden layers in the network.

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BibTeX RIS

Haoxing Tian, Ioannis Ch. Paschalidis, Alex Olshevsky. 2023-12-08. On the Performance of Temporal Difference Learning With Neural Networks. https://arxiv.org/abs/2312.05397

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