実世界強化学習の9つの課題
Challenges of Real-World Reinforcement Learning
強化学習を実世界に応用する際に障壁となる9つの課題を整理し、各課題の意味・既存手法・評価指標を示すとともに、それらを再現するテストベッド環境を提案した論文。
著者: Gabriel Dulac-Arnold, Daniel Mankowitz, Todd Hester
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are often hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. We present a set of nine unique challenges that must be addressed to productionize RL to real world problems. For each of these challenges, we specify the exact meaning of the challenge, present some approaches from the literature, and specify some metrics for evaluating that challenge. An approach that addresses all nine challenges would be applicable to a large number of real world problems. We also present an example domain that has been modified to present these challenges as a testbed for practical RL research.
