The Cross-environment Hyperparameter Setting Benchmark for Reinforcement
Learning
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement
Learning
This paper introduces a new empirical methodology, the Cross-environment Hyperparameter Setting Benchmark, that compares RL algorithms across environments using a single hyperparameter setting, encouraging algorithmic development which is insensitive to hyperparameters. We demonstrate that this benchmark is robust to statistical noise and obtains qualitatively similar results across repeated applications, even …