A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning

Published: 25 Sept 2024, Last Modified: 23 Jan 2025NeurIPS 2024 posterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Reinforcement Learning, Hyperparameters, Empirical Methodology
TL;DR: An empirical methodology for evaluating hyperparameter sensitivity of reinforcement learning algorithms.
Abstract: The performance of modern reinforcement learning algorithms critically relies on tuning ever increasing numbers of hyperparameters. Often, small changes in a hyperparameter can lead to drastic changes in performance, and different environments require very different hyperparameter settings to achieve state-of-the-art performance reported in the literature. We currently lack a scalable and widely accepted approach to characterizing these complex interactions. This work proposes a new empirical methodology for studying, comparing, and quantifying the sensitivity of an algorithm’s performance to hyperparameter tuning for a given set of environments. We then demonstrate the utility of this methodology by assessing the hyperparameter sensitivity of several commonly used normalization variants of PPO. The results suggest that several algorithmic performance improvements may, in fact, be a result of an increased reliance on hyperparameter tuning.
Primary Area: Reinforcement learning
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Submission Number: 13872
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