On Experiences in a Complex and Competitive Gaming Domain: Reinforcement Learning Meets RoboCup

Martin A. Riedmiller, T. Gabel
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引用次数: 57

Abstract

RoboCup soccer simulation features the challenges of a fully distributed multi-agent domain with continuous state and action spaces, partial observability, as well as noisy perception and action execution. While the application of machine learning techniques in this domain represents a promising idea in itself, the competitive character of RoboCup also evokes the desire to head for the development of learning algorithms that are more than just a proof of concept. In this paper, we report on our experiences and achievements in applying reinforcement learning (RL) methods in the scope of our Brainstormers competition team within the Simulation League of RoboCup during the past years
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关于复杂和竞争性游戏领域的经验:强化学习与机器人世界杯
RoboCup足球模拟的特点是具有连续状态和动作空间、部分可观察性以及噪声感知和动作执行的完全分布式多智能体领域的挑战。虽然机器学习技术在这一领域的应用本身就代表着一个很有前途的想法,但机器人世界杯的竞争特点也唤起了人们对学习算法发展的渴望,而不仅仅是概念的证明。在本文中,我们报告了我们在过去几年中在机器人世界杯模拟联赛的头脑风暴竞赛团队范围内应用强化学习(RL)方法的经验和成就
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