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Front. Inform. Technol. Electron. Eng.  2010, Vol. 11 Issue (9): 718-723    DOI: 10.1631/jzus.C0910486
    
Modified reward function on abstract features in inverse reinforcement learning
Shen-yi Chen*, Hui Qian, Jia Fan, Zhuo-jun Jin, Miao-liang Zhu
School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Modified reward function on abstract features in inverse reinforcement learning
Shen-yi Chen*, Hui Qian, Jia Fan, Zhuo-jun Jin, Miao-liang Zhu
School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
 全文: PDF 
摘要： We improve inverse reinforcement learning (IRL) by applying dimension reduction methods to automatically extract abstract features from human-demonstrated policies, to deal with the cases where features are either unknown or numerous. The importance rating of each abstract feature is incorporated into the reward function. Simulation is performed on a task of driving in a five-lane highway, where the controlled car has the largest fixed speed among all the cars. Performance is almost 10.6% better on average with than without importance ratings.
关键词： Importance rating;  Abstract feature;  Feature extraction;  Inverse reinforcement learning (IRL);  Markov decision process (MDP)    
Abstract: We improve inverse reinforcement learning (IRL) by applying dimension reduction methods to automatically extract abstract features from human-demonstrated policies, to deal with the cases where features are either unknown or numerous. The importance rating of each abstract feature is incorporated into the reward function. Simulation is performed on a task of driving in a five-lane highway, where the controlled car has the largest fixed speed among all the cars. Performance is almost 10.6% better on average with than without importance ratings.
Key words: Importance rating    Abstract feature    Feature extraction    Inverse reinforcement learning (IRL)    Markov decision process (MDP)
收稿日期: 2009-08-07 出版日期: 2010-09-07
CLC:  TP181  
通讯作者: Shen-yi CHEN     E-mail: charles_csy@zju.edu.cn
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Shen-yi Chen, Hui Qian, Jia Fan, Zhuo-jun Jin, Miao-liang Zhu. Modified reward function on abstract features in inverse reinforcement learning. Front. Inform. Technol. Electron. Eng., 2010, 11(9): 718-723.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C0910486    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2010/V11/I9/718

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