Reinforcement learning for robot soccer
Autonomous Robots
On the use of human instruction for improving the behavior of RoboCup soccer agents
Artificial Life and Robotics
Cognitive concepts in autonomous soccer playing robots
Cognitive Systems Research
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While a lot of papers on RoboCup's robotic 2D soccer simulation have focused on the players' offensive behavior, there are only a few papers that specifically address a team's defense strategy. In this paper, we consider a defense scenario of crucial importance: We focus on situations where one of our players must interfere and disturb an opponent ball leading player in order to scotch the opponent team's attack at an early stage and, even better, to eventually conquer the ball initiating a counter attack. We employ a reinforcement learning methodology that enables our players to autonomously acquire such an aggressive duel behavior, and we have embedded it into our soccer simulation team's defensive strategy. Employing the learned NeuroHassle policy in our competition team, we were able to clearly improve the capabilities of our defense and, thus, to increase the performance of our team as a whole.