Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning
This addresses the challenge of performance variability in reinforcement learning for researchers and practitioners, but it is incremental as it builds on existing hyperparameter optimization methods.
The study investigated whether hyperparameter optimization for deep reinforcement learning should prioritize exploring many settings via pruning or using repetitions for quality, finding that pruning can harm optimization and repetitions do not improve performance across random seeds, with Bayesian optimization using a noise-robust acquisition function identified as the best method.
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. In particular, we benchmark whether it is better to explore a large quantity of hyperparameter settings via pruning of bad performers, or if it is better to aim for quality of collected results by using repetitions. For this we consider the Successive Halving, Random Search, and Bayesian Optimization algorithms, the latter two with and without repetitions. We apply these to tuning the PPO2 algorithm on the Cartpole balancing task and the Inverted Pendulum Swing-up task. We demonstrate that pruning may negatively affect the optimization and that repeated sampling does not help in finding hyperparameter settings that perform better across random seeds. From our experiments we conclude that Bayesian optimization with a noise robust acquisition function is the best choice for hyperparameter optimization in reinforcement learning tasks.