Holiday
DELTA台達106 T2T3T4
This course covers from the fundamental concepts of deep reinforcement learning (DRL), to the state-of-the-art reinforcement learning methodologies. The target of this course is to train students to learn and implement DRL models, understanding the concepts and trade-offs of them, as well as applying them to different evaluation scenarios and environments. The course schedule is intense, containing lots of assignments, projects, as well as midterm and final exams.
Course keywords: Deep reinforcement learning. (深度增強式學習), Deep learning. (深度學習), Machine learning. (機器學習), Artificial intelligence. (人工智慧), Intelligent agents. (智慧型代理人) 一、課程說明(Course Description) This course covers from the fundamental concepts of deep reinforcement learning (DRL), to the state-of-the-art reinforcement learning methodologies. The target of this course is to train students to learn and implement DRL models, understanding the concepts and trade-offs of them, as well as applying them to different evaluation scenarios and environments. The course schedule is intense, containing lots of assignments and projects. Prerequisites: This course will assume some familiarity with linear algebra, probabilities, numerical optimization, and machine learning. 二、指定用書(Text Books) Sutton & Barto, Reinforcement Learning: An Introduction. 三、參考書籍(References) - Deep learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville. - David Silver's course on reinforcement learning. - Sergey Levine’s CS294 course from UC Berkeley. 四、教學方式(Teaching Method) This course will be taught in class. Each week has three hours, consisting of two hours of lectures and one hour of instructor/TA lead hands-on experiments. The course will have an online forum for Q&A and discussion. The instructor will offer papers for students to read every week. There will be 6~7 homeworks. For each homework, we will post a PDF on ILMS and starter code on Github. We will also post slides on ILMS for each lecture. 五、教學進度(Syllabus) - Introduction to deep learning - Introduction to multi-armed bandits - Markov decision process - Value function approximation - TD Lambda - Deep Q-learning - Policy gradients - Actor-critic - Inverse reinforcement learning - A3C, TRPO, and PPO - Exploration techniques - Advanced topics in DRL - DRL in robotics 六、成績考核(Evaluation) - 6~7 homework assignments 70% - Final project 30% 七、可連結之網頁位址 相關網頁(Personal Website) Elsalab.ai 八、採用下列何項 AI 使用規則 有條件開放,請註明如何使用生成式AI於課程產出 Conditionally open; please specify how generative AI will be used in course output
MON | TUE | WED | THU | FRI | |
08:00108:50 | |||||
09:00209:50 | |||||
10:10311:00 | |||||
11:10412:00 | |||||
12:10n13:00 | |||||
13:20514:10 | |||||
14:20615:10 | |||||
15:30716:20 | |||||
16:30817:20 | |||||
17:30918:20 | |||||
18:30a19:20 | |||||
19:30b20:20 | |||||
20:30c21:20 |
Average Percentage 87.54
Std. Deviation 7.23
16週課程。以加簽方式選課。先修課程:深度學習、機器學習。
-
-