Reinforcement learning vs human programming in tetherball robot games
Reinforcement learning vs human programming in tetherball robot games
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DOI：
10.1109/iros.2015.7354296
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发表时间：
2015-12
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影响因子：
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通讯作者：
Simone Parisi;Hany Abdulsamad;A. Paraschos;Christian Daniel;Jan Peters
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文献类型：
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作者：
Simone Parisi;Hany Abdulsamad;A. Paraschos;Christian Daniel;Jan Peters
为了赋予机器人学习广泛技能和解决复杂任务的能力，运动技能的强化学习是一个重要的挑战。然而，将强化学习与人类编程进行比较并不简单。在本文中，我们创建了一个电机学习框架，包括国家的最先进的组件在电机技能学习和比较它的机器人绳球任务的手动设计的程序。我们使用动态电机原语表示机器人的轨迹和相对熵策略搜索来训练电机框架，并通过尝试和错误来改善其行为。这些算法组件允许高质量的技能学习，而实验设置可以准确评估我们的框架，因为机器人玩家可以相互竞争。在机器人绳球的复杂游戏中，我们证明了我们的学习方法优于高质量的手工制作的系统，并赢得了比赛。
Reinforcement learning of motor skills is an important challenge in order to endow robots with the ability to learn a wide range of skills and solve complex tasks. However, comparing reinforcement learning against human programming is not straightforward. In this paper, we create a motor learning framework consisting of state-of-the-art components in motor skill learning and compare it to a manually designed program on the task of robot tetherball. We use dynamical motor primitives for representing the robot's trajectories and relative entropy policy search to train the motor framework and improve its behavior by trial and error. These algorithmic components allow for high-quality skill learning while the experimental setup enables an accurate evaluation of our framework as robot players can compete against each other. In the complex game of robot tetherball, we show that our learning approach outperforms and wins a match against a high quality hand-crafted system.