Gray-Box Gaussian Processes for Automated Reinforcement LearningDownload PDF

Anonymous

22 Sept 2022, 12:42 (modified: 18 Nov 2022, 22:17)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Abstract: Despite having achieved spectacular milestones in an array of important real-world applications, most Reinforcement Learning (RL) methods are very brittle concerning their hyperparameters. Notwithstanding the crucial importance of setting the hyperparameters in training state-of-the-art agents, the task of hyperparameter optimization (HPO) in RL is understudied. In this paper, we propose a novel gray-box Bayesian Optimization technique for HPO in RL, that enriches Gaussian Processes with reward curve estimations based on generalized logistic functions. In a very large-scale experimental protocol, comprising 5 popular RL methods (DDPG, A2C, PPO, SAC, TD3), dozens of environments (Atari, Mujoco), and 7 HPO baselines, we demonstrate that our method significantly outperforms current HPO practices in RL.
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