深層強化学習によるサッカーロボットのドリブル動作獲得と実環境への応用に向けた基礎実験

抄録

The goal of this research is to acquire dribbling behavior of a soccer robot using deep reinforcement learning. We constructed a simulated soccer field environment, in which a wheeled robot successfully learned to dribble by itself. We also tackle the issue of observation uncertainty, which is one of the challenges in applying reinforcement learning to real environments. In this paper two settings are considered: 1) the robot needs to observe landmarks and estimate self-position, 2) the field of view of the ball observation is limited. We observed significant performance drop under the condition that the robot’s field of view is limited.