Teaching agents with human feedback: a demonstration of the TAMER framework.
IUI(2013)
摘要
ABSTRACTIncorporating human interaction into agent learning yields two crucial benefits. First, human knowledge can greatly improve the speed and final result of learning compared to pure trial-and-error approaches like reinforcement learning. And second, human users are empowered to designate "correct" behavior. In this abstract, we present research on a system for learning from human interaction - the TAMER framework - then point to extensions to TAMER, and finally describe a demonstration of these systems.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要