実ロボット上の深層強化学習アルゴリズム評価による再現性に関する調査
A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots
強化学習研究の再現性を妨げる要因を整理し、実ロボット実験で標準化された評価手法と適切な統計処理の重要性を示した調査論文。
著者: Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, John Hallam
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
As reinforcement learning (RL) achieves more success in solving complex tasks, more care is needed to ensure that RL research is reproducible and that algorithms herein can be compared easily and fairly with minimal bias. RL results are, however, notoriously hard to reproduce due to the algorithms' intrinsic variance, the environments' stochasticity, and numerous (potentially unreported) hyper-parameters. In this work we investigate the many issues leading to irreproducible research and how to manage those. We further show how to utilise a rigorous and standardised evaluation approach for easing the process of documentation, evaluation and fair comparison of different algorithms, where we emphasise the importance of choosing the right measurement metrics and conducting proper statistics on the results, for unbiased reporting of the results.