モデルベース品質多様性探索による効率的なロボット学習
Model-Based Quality-Diversity Search for Efficient Robot Learning
パラメータ摂動の挙動を予測するニューラルネットを品質多様性(QD)探索に統合し、見込みのない行動の実行を避けることでサンプル効率とスキル適応を改善した研究。
著者: Leon Keller, Daniel Tanneberg, Svenja Stark, Jan Peters
分類: cs.AI, cs.LG, cs.NE, cs.RO
原文アブストラクト
Despite recent progress in robot learning, it still remains a challenge to program a robot to deal with open-ended object manipulation tasks. One approach that was recently used to autonomously generate a repertoire of diverse skills is a novelty based Quality-Diversity~(QD) algorithm. However, as most evolutionary algorithms, QD suffers from sample-inefficiency and, thus, it is challenging to apply it in real-world scenarios. This paper tackles this problem by integrating a neural network that predicts the behavior of the perturbed parameters into a novelty based QD algorithm. In the proposed Model-based Quality-Diversity search (M-QD), the network is trained concurrently to the repertoire and is used to avoid executing unpromising actions in the novelty search process. Furthermore, it is used to adapt the skills of the final repertoire in order to generalize the skills to different scenarios. Our experiments show that enhancing a QD algorithm with such a forward model improves the sample-efficiency and performance of the evolutionary process and the skill adaptation.
