適用於小精靈的深度強化式學習之研究

Abstract

深度類神經網路（Deep Neural Network）是２００６年開始發展的一種機器學習技術。近年來，深度類神經網路被廣泛利用在資訊工程領域的各種應用上，並獲得出色的成績。 本論文將深度類神經網路應用在遊玩Ms. Pac-Man的遊戲上。深度加強學習（Deep Reinforcement Learning）是一種結合深度類神經網路與Q學習方法（Q-learning）的技術，也是加強學習的一個變體。此研究將Ms. Pac-Man遊戲中抽象化後的資訊當成類神經網路的輸入，使用其網路的人工智慧程式，可以在超過90%的嘗試中通過前兩個關卡。在最短通關時間與最佳通關分數兩項數據上，深度加強學習方法與之前的蒙地卡羅搜尋樹（Monte-Carlo Tree Search）方法相比，有顯著的進步。

Deep Neural Network (DNN), a branch of machine learning was introduced in 2006, have had remarkable success in of computer science. DNN can be applied to solving a wide range of problems. Deep Reinforcement Learning (DRL) is a combination of DNN and Q-learning, a form of Reinforcement Learning technique. This thesis applies DRL to create a program playing Ms. Pac-Man game. This study uses the abstracted information of Ms. Pac-Man game as the input of the network. Our program can pass the first level at a rate of 99.10%, the second at 91.20%, and the third at 82.60%. The performance of DRL method is significantly better than Monte Carlo Tree Search (MCTS) in terms of both time and score.