Teaching robots behavior patterns by using reinforcement learning: how to raise pet robots with a remote control
Teaching robots behavior patterns by using reinforcement learning: how to raise pet robots with a remote control
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使用强化学习教授机器人行为模式：如何用遥控器饲养宠物机器人
DOI：
10.11499/sicep.2004.0.97.1
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
M. Mizukawa
中科院分区：
文献类型：
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作者：
Mons O Ullerstam;M. Mizukawa
该项目的目标是证明复杂的行为模式可以通过基于强化学习的系统来学习。具体的任务是让索尼机器狗AIBO根据人类和AIBO之间的互动学习复杂的行为模式。强化学习系统通过远程控制进行教学，由人类使用并连接到AIBO。为了记住学习的行为序列，AIBO使用先前动作的短期记忆。本文证明了在复杂环境中学习行为序列和因果关系是可能的。该论文还表明，该系统在自然环境中工作，基于人类与AIBO之间的互动，并行学习奖励和获取奖励的方法。AIBO还能够通过使用我们称之为“即时学习”的方法立即学习新的行为。本文介绍了实现这样一个系统的方法。
The goal of this project was to show that complex behavior patterns can be learnt by a system based on reinforcement learning. The specific task was to make AIBO, the Sony robot dog, learn complex behavior patterns based on interactions between humans and AIBO. The reinforcement learning system is taught by remote control, used by the human and connected to AIBO. To remember the learnt behavior sequences, a short-term memory of prior actions is used by AIBO. This paper demonstrates that it is possible to learn behavior sequences and the relationship of cause and effect in complex environments. The paper also shows that the system works in a natural environment, based on the interaction between humans and AIBO, learning the rewards and the means to reach them in parallel. AIBO is also able to pick up new behaviors instantly by using a method we call 'Instant learning'. The paper presents the methods for implementing such a system.