A Large Deviations Perspective on Policy Gradient Algorithms
Abstract
Motivated by policy gradient methods in the context of reinforcement learning, we derive the first large deviation rate function for the iterates generated by stochastic gradient descent for possibly non-convex objectives satisfying a Polyak-Łojasiewicz condition. Leveraging the contraction principle from large deviations theory, we illustrate the potential of this result by showing how convergence properties of policy gradient with a softmax parametrization and an entropy regularized objective can be naturally extended to a wide spectrum of other policy parametrizations.
Cite
Text
Jongeneel et al. "A Large Deviations Perspective on Policy Gradient Algorithms." Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024.Markdown
[Jongeneel et al. "A Large Deviations Perspective on Policy Gradient Algorithms." Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024.](https://mlanthology.org/l4dc/2024/jongeneel2024l4dc-large/)BibTeX
@inproceedings{jongeneel2024l4dc-large,
title = {{A Large Deviations Perspective on Policy Gradient Algorithms}},
author = {Jongeneel, Wouter and Kuhn, Daniel and Li, Mengmeng},
booktitle = {Proceedings of the 6th Annual Learning for Dynamics & Control Conference},
year = {2024},
pages = {916-928},
volume = {242},
url = {https://mlanthology.org/l4dc/2024/jongeneel2024l4dc-large/}
}