Improving Deep Policy Gradients with Value Function SearchDownload PDF


22 Sept 2022, 12:35 (modified: 11 Nov 2022, 15:17)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: Deep Reinforcement Learning, Deep Policy Gradients
TL;DR: We present a Value Function Search that employs a gradient-free population of perturbed value networks to improve Deep Policy Gradient primitives, leading to higher returns and better sample efficiency.
Abstract: Deep Policy Gradient algorithms employ value networks to drive the learning of parameterized policies and reduce the variance of the gradient estimates. However, value function approximation gets stuck in local optima and struggles to fit the actual return, limiting the variance reduction efficacy and leading policies to sub-optimal performance. In this paper, we focus on improving value approximation and analyzing the effects on Deep Policy Gradient primitives such as value prediction, variance reduction, and correlation of gradient estimates with the true gradient. To this end, we introduce a Value Function Search that employs a population of perturbed value networks to search for a better approximation. Our framework does not require additional environment interactions, gradient computations, or ensembles, providing a computationally inexpensive approach to enhance the supervised learning task on which value networks train. Crucially, we show that improving Deep Policy Gradient primitives results in improved sample efficiency and policies with higher returns using standard policy gradient methods on common continuous control benchmark domains.
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