探索分布の設計で歩容を学ぶ：四足歩行のウォークとトロット
Learning walk and trot from the same objective using different types of exploration
ガウス探索分布の初期共分散に歩容の対称性を組み込み、同じ目的関数から四足ロボットのウォークとトロットを学習させ、性能を向上させた研究。
著者: Zinan Liu, Kai Ploeger, Svenja Stark, Elmar Rueckert, Jan Peters
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
In quadruped gait learning, policy search methods that scale high dimensional continuous action spaces are commonly used. In most approaches, it is necessary to introduce prior knowledge on the gaits to limit the highly non-convex search space of the policies. In this work, we propose a new approach to encode the symmetry properties of the desired gaits, on the initial covariance of the Gaussian search distribution, allowing for strategic exploration. Using episode-based likelihood ratio policy gradient and relative entropy policy search, we learned the gaits walk and trot on a simulated quadruped. Comparing these gaits to random gaits learned by initialized diagonal covariance matrix, we show that the performance can be significantly enhanced.
