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URN etd-1028114-170500
Author Tien-yu Cheng
Author's Email Address No Public.
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Department Electrical Engineering
Year 2014
Semester 1
Degree Master
Type of Document
Language English
Title Inverse Reinforcement Learning based on Critical State
Date of Defense 2014-11-22
Page Count 57
Keyword
  • reward feature construction
  • Apprenticeship Learning
  • Inverse Reinforcement learning
  • reward function
  • Reinforcement learning
  • Abstract Reinforcement Learning (RL) makes an agent learn through interacting with a dynamic environment. One fundamental assumption of existing RL algorithms is that reward function, the most succinct representation of the designer’s intention, needs to be provided beforehand. It is difficult to provide appropriate reward functions in complex problems. The goal of the inverse reinforcement learning is finding a reward function in Markov Decision Process. A set of reward indexes and good example traces demonstrated by expert are needed in an IRL process. However, it is difficult to select a set of reward indexes in complex problems. In this thesis, Inverse Reinforcement Learning based on Critical State (IRLCS) algorithm is proposed to search a succinct and meaningful reward function. IRLCS select a set of reward indexes from whole state space through comparing the difference between the good and bad demonstrations. According to the results of experiment, IRLCS can find a good strategy that closes to the expert strategy. Besides, IRLCS save a lot of computational time. The Research results are presented by the video at YouTube: http://youtu.be/cMaOdoTt4Hw .
    Advisory Committee
  • Jin-Ling Lin - chair
  • Ming-Yi Ju - co-chair
  • Yu-Jen Chen - co-chair
  • Kao-Shing Hwang - advisor
  • Files
  • etd-1028114-170500.pdf
  • indicate access worldwide
    Date of Submission 2014-11-28

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