國 立 陽 明 交 通 大 學 演 講
講 題： 深度強化學習在遊戲AI的進展 (Advances in Deep Reinforcement Learning for Game AI)
主 講 人： 吳廸融(Ti-Rong Wu)博士
中央研究院-資訊科學研究所
主 持 人： 陳勝一 副教授
主辦單位： 陽明交通大學工業工程與管理系
演講時間： 114年4月14(星期一)13: 20 ~ 15:10
地點： 管理二館MB520室
摘要：Deep Reinforcement Learning (DRL) has achieved significant advancements in various domains, such as game-playing, robotics, and natural language processing. Among these fields, game-playing serve as an important testing ground for DRL algorithms due to their controllable and accessible environments that stand in contrast to the complex real-world problems. For example, in 2016, AlphaGo integrated DRL with search algorithms and beat world champion Lee Sedol. Following the success of AlphaGo, recent DRL algorithms, like AlphaZero and MuZero, have demonstrated super-human performance in numerous computer games without using any human-derived knowledge. This talk will begin with an introduction to the foundational concepts of deep reinforcement learning. We will then discuss various reinforcement learning algorithms used in game-playing, followed by recent advances across several directions in DRL and game AI.

演講性質：學術研究專題
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