自動環境整形こそが強化学習の次のフロンティア
Automatic Environment Shaping is the Next Frontier in RL
ロボット強化学習の主要なボトルネックはアルゴリズムではなく、観測・行動・報酬・シミュレーション力学といった訓練環境の設計（環境整形）であり、その自動化を研究コミュニティが目指すべきだと主張するポジションペーパー。
著者: Younghyo Park, Gabriel B. Margolis, Pulkit Agrawal
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Many roboticists dream of presenting a robot with a task in the evening and returning the next morning to find the robot capable of solving the task. What is preventing us from achieving this? Sim-to-real reinforcement learning (RL) has achieved impressive performance on challenging robotics tasks, but requires substantial human effort to set up the task in a way that is amenable to RL. It's our position that algorithmic improvements in policy optimization and other ideas should be guided towards resolving the primary bottleneck of shaping the training environment, i.e., designing observations, actions, rewards and simulation dynamics. Most practitioners don't tune the RL algorithm, but other environment parameters to obtain a desirable controller. We posit that scaling RL to diverse robotic tasks will only be achieved if the community focuses on automating environment shaping procedures.
