NDLI: Learning behavior styles with inverse reinforcement learning

Content Provider	ACM Digital Library
Author	Lee, Seong Jae Popović, Zoran
Copyright Year	2010
Abstract	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.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISSN	07300301
e-ISSN	15577368
DOI	10.1145/1778765.1778859
Volume Number	29
Issue Number	4
Journal	ACM Transactions on Graphics (TOG)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-26
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Apprenticeship learning Data driven animation Human animation Inverse reinforcement learning Optimal control
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design

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Learning behavior styles with inverse reinforcement learning