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Title page for etd-0103108-161220


URN etd-0103108-161220 Statistics This thesis had been viewed 2098 times. Download 16 times.
Author Tang-Xun Guan
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2007 Semester 1
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 35
Title Improving Learning Efficacy of Reinforcement Learning from Seniors' Knowledge ─ A Case Study on TD-GAMMON
Keyword
  • TD-Gammon
  • Reinforcement learning
  • Reinforcement learning
  • TD-Gammon
  • Abstract Reinforcement learning is a special machine learning method. It is different from supervised learning. It can learn by self-play without using examples. This represents that it can study in an unknown environment, but supervised learning can’t.
    But the questions we usually meet aren’t located in the unknown environment. In other words, we usually have a lot of experiences that can be referred. Take chess as the example, we have many sources of experiences. In this case, if we don’t use those knowledge may waste those resources. Therefore the goal we study is how to combine self-play and those knowledge.
    We take TD-Gammon as the example, TD-Gammon is one of the most impressive applications of reinforcement learning. The learning algorithm in TD-Gammon was a straightforward combination of the TD($\lambda $) algorithm and nonlinear function approximation using a multilayer neural network trained by backpropagating TD errors. TD-Gammon learned to play extremely well, near the level of the world's strongest grandmasters. We entered seniors' knowledge in TD-Gammon self-play process, and improving learning efficacy. Moreover, we attempt improving learning efficacy further by using more seniors.
    Keywords: Reinforcement learning; TD-Gammon
    Advisor Committee
  • Tai-Wen Yue - advisor
  • none - co-chair
  • Wei Yen - co-chair
  • Files indicate in-campus access only
    Date of Defense 2008-01-02 Date of Submission 2008-01-03


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