AutoRL Hyperparameter LandscapesDownload PDF

Published: 16 May 2023, Last Modified: 05 Sept 2023AutoML 2023 MainTrackReaders: Everyone
TL;DR: Using Landscapes to model sequential hyperparameter configurations for Automated Reinforcement Learning
Abstract: Although Reinforcement Learning (RL) has shown to be capable of producing impressive results, its use is limited by the impact of its hyperparameters on performance. This often makes it difficult to achieve good results in practice. Automated RL (AutoRL) addresses this difficulty, yet little is known about the dynamics of the hyperparameter landscapes that hyperparameter optimization (HPO) methods traverse in search of optimal configurations. In view of existing AutoRL approaches dynamically adjusting hyperparameter configurations, we propose an approach to build and analyze these hyperparameter landscapes not just for one point in time but at multiple points in time throughout training. Addressing an important open question on the legitimacy of such dynamic AutoRL approaches, we provide thorough empirical evidence that the hyperparameter landscapes strongly vary over time across representative algorithms from RL literature (DQN, PPO and SAC) in different kinds of environments (Cartpole, Bipedal Walker and Hopper). This supports the theory that hyperparameters should be dynamically adjusted during training and shows the potential for more insights on AutoRL problems that can be gained through landscape analysis. Our code can be found at
Keywords: Reinforcement Learning, Automated Reinforcement Learning, Hyperparameter Landscapes
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