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URN	etd-1028114-170500
Author	Tien-yu Cheng
Author's Email Address	No Public.
Statistics	This thesis had been viewed 5351 times. Download 593 times.
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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