.png){kind=icon}
返回
Learning Behavior Styles with Inverse Reinforcement Learning
.png){kind=icon}
PRE
AI
DOI：10.1145/1778765.1778859.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a method for inferring the behavior styles of character controllers from a small set of examples. We show that a rich set of behavior variations can be captured by determining the appropriate reward function in the reinforcement learning framework, and show that the discovered reward function can be applied to different environments and scenarios. We also introduce a new algorithm to recover the unknown reward function that improves over the original apprenticeship learning algorithm. We show that the reward function representing a behavior style can be applied to a variety of different tasks, while still preserving the key features of the style present in the given examples. We describe an adaptive process where an author can, with just a few additional examples, refine the behavior so that it has better generalization properties.
Keyword:
Optimal Control
Data Driven Animation
Human Animation
Inverse Reinforcement Learning
Apprenticeship Learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.5
论文数:
4.9K
被引数:
3.6W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容