制御可能性を考慮した教師なしスキル発見
Controllability-Aware Unsupervised Skill Discovery
現在のスキルで達成しにくい状態遷移を優先する距離関数を導入し、複雑で制御が難しいスキルを教師なしで発見する手法CSDを提案。ロボット操作・移動環境で多様な複雑スキルを獲得できることを示した。
著者: Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
One of the key capabilities of intelligent agents is the ability to discover useful skills without external supervision. However, the current unsupervised skill discovery methods are often limited to acquiring simple, easy-to-learn skills due to the lack of incentives to discover more complex, challenging behaviors. We introduce a novel unsupervised skill discovery method, Controllability-aware Skill Discovery (CSD), which actively seeks complex, hard-to-control skills without supervision. The key component of CSD is a controllability-aware distance function, which assigns larger values to state transitions that are harder to achieve with the current skills. Combined with distance-maximizing skill discovery, CSD progressively learns more challenging skills over the course of training as our jointly trained distance function reduces rewards for easy-to-achieve skills. Our experimental results in six robotic manipulation and locomotion environments demonstrate that CSD can discover diverse complex skills including object manipulation and locomotion skills with no supervision, significantly outperforming prior unsupervised skill discovery methods. Videos and code are available at https://seohong.me/projects/csd/
