Acquiring Dribbling Skill by a Soccer Robot via Deep Reinforcement Learning and Preliminary Study Towards Real World Application

Abstract

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.