Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning

29 Jul 2020 Lars Hertel Pierre Baldi Daniel L. Gillen

Reinforcement learning algorithms can show strong variation in performance between training runs with different random seeds. In this paper we explore how this affects hyperparameter optimization when the goal is to find hyperparameter settings that perform well across random seeds... (read more)

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