On the Performance of Temporal Difference Learning With Neural Networks

• URL: http://arxiv.org/abs/2312.05397v1
• Date: Fri, 8 Dec 2023 22:34:29 GMT
• Title: On the Performance of Temporal Difference Learning With Neural Networks
• Authors: Haoxing Tian, Ioannis Ch. Paschalidis, Alex Olshevsky
• Abstract summary: TD Learning is an approximate temporal difference method for policy evaluation that uses a neural network for function approximation. We show an approximation bound of $O(epsilon) + tildeO (1/sqrtm)$ where $epsilon$ is the approximation quality of the best neural network.
• Score: 20.721853144434743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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