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作者(中文):陳怡靜
作者(外文):Chen, Yi-Ching.
論文名稱(中文):應用強化獎勵機制學習解魔術方塊
論文名稱(外文):Solving Rubik's Cube by Policy Gradient Based Reinforcement Learning
指導教授(中文):林永隆
指導教授(外文):Lin, Youn-Long
口試委員(中文):陳煥宗
黃俊達
口試委員(外文):Chen, Hwann-Tzong
Huang, Juinn-Dar
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系所
學號:105062614
出版年(民國):107
畢業學年度:107
語文別:英文
論文頁數:30
中文關鍵詞:強化學習魔術方塊策略梯度
外文關鍵詞:Reinforcement LearningRubik's CubePolicy Gradient
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強化學習系統提供了代理人與環境互動機制,策略梯度方法目的在於儘可能採
取好的動作。我們提出一個在強化學習系統上運用線性的策略梯度方法和強化獎
懲機制進而達到對於好的動作有較高的機率。實驗結果顯示此方法用神經網路模式
可以解部分的魔術方塊問題,但是仍不能解所有問題。
Reinforcement Learning provides a mechanism for training an agent to interact with its environment. Policy gradient makes the right actions more probable. We propose using a linear policy gradient method in a deep neural network-based reinforcement learning. The proposed method employs an intensifying reward function to increase the probabilities of right actions to solve the Rubik's Cube problems. Experiments show that our proposed neural network learned to solve some Rubik's Cube states. For more difficult initial states, the network still cannot always give the correct suggestion.
Abstract i
Contents ii
List of Tables iii
List of Figures iv
1 Motivation 1
2 Related Work 3
3 Reinforcement Learning 5
3.1 Basic concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.2 Policy Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4 Proposed Methodology and Implementation 8
4.1 Data Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.2 Basic Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5 Experiment Results 17
6 Conclusion and Future Work 26
References 28
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no. 7587, pp. 484{489, 2016.
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[17] D. Shah, "Activation Functions." [Online]. Available: https://
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Actor-critic introduction." Retrieved August, 2017, from UC Berkeley Web
site: http://rll.berkeley.edu/deeprlcourse/f17docs/lecture_5_actor_
critic_pdf.pdf.
 
 
 
 
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