下載電子全文宣告This thesis is authorized to indicate in-campus access only
You can not download at the moment.
Your IP address is 22.214.171.124
The defense date of the thesis is 2008-01-03
The current date is 2019-07-19
This thesis will be accessible at off-campus not accessible
URN etd-0103108-161220 Statistics This thesis had been viewed 2153 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 Date of Defense 2008-01-02 Date of Submission 2008-01-03