強化學習
Reinforcement Learning
學期
109-2
學分
3
學分
當期課號
5246
永久課號
IOG5029
開課單位
智慧科學暨綠能學院
授課教師
林勻蔚
校區
歸仁
類別
選修
上課時間表
| 節 | 週三 |
|---|---|
5 13:20–14:10 | 強化學習 CM214 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
概述
This course provides a clear and simple account of the key ideas and algorithms of reinforcement learning and takes the point of view of artificial intelligence and engineering. We also survey some of the frontiers of reinforcement learning in biology and applications.
先修科目
Python
評分方式
In-class projects (60%) Final project (40%)
課程大綱
- Tabular Solution Methods Introduction
- Approximate Solution Methods Introduction
- Frontiers
- The Reinforcement Learning Problem
週次計畫
| 週次 | 主題 |
|---|---|
| 第 1 週 | The Reinforcement Learning Problem |
| 第 2 週 | Tabular Solution Methods Introduction |
| 第 3 週 | Multi-arm Bandits |
| 第 4 週 | Finite Markov Decision Processes |
| 第 5 週 | Dynamic Programming |
| 第 6 週 | Monte Carlo Methods |
| 第 7 週 | Temporal-Difference Learning |
| 第 8 週 | Eligibility Traces |
| 第 9 週 | Planning and Learning with Tabular Methods Introduction |
| 第 10 週 | Approximate Solution Methods Introduction |
| 第 11 週 | On-policy Approximation of Action Values |
| 第 12 週 | Off-policy Approximation of Action Values |
| 第 13 週 | Policy Approximation |
| 第 14 週 | Psychology |
| 第 15 週 | Neuroscience |
| 第 16 週 | Applications and Case Studies |
| 第 17 週 | Final Project Presentation |
| 第 18 週 | Final Project Demonstration |
Office Hours
- 地點
- 422
- 時間
- 10:00~12:00, Wed.
- 聯絡方式
- 03-5731350