強化學習
Reinforcement Learning
| 節 | 週三 |
|---|---|
5 13:20–14:10 | 強化學習 CM216（歸仁） 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
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.
Python
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In-class projects (60%) Final project (40%)
The Reinforcement Learning Problem
The Reinforcement Learning Problem Introduction
- 講授：
- 6
Tabular Solution Methods Introduction
Multi-arm Bandits Finite Markov Decision Processes Dynamic Programming Monte Carlo Methods Temporal-Difference Learning Eligibility Traces Planning and Learning with Tabular Methods
- 講授：
- 16
- 實作：
- 8
Approximate Solution Methods Introduction
On-policy Approximation of Action Values Off-policy Approximation of Action Values Policy Approximation
- 講授：
- 8
- 實作：
- 4
Frontiers
Psychology Neuroscience Applications and Case Studies
- 講授：
- 8
- 實作：
- 4
| 週次 | 主題 |
|---|---|
| 第 1 週 | The Reinforcement Learning Problem 2023-02-15(三) |
| 第 2 週 | Tabular Solution Methods Introduction 2023-02-22(三) |
| 第 3 週 | Multi-arm Bandits 2023-03-01(三) |
| 第 4 週 | Finite Markov Decision Processes 2023-03-08(三) |
| 第 5 週 | Dynamic Programming 2023-03-15(三) |
| 第 6 週 | Monte Carlo Methods 2023-03-22(三) |
| 第 7 週 | Temporal-Difference Learning 2023-03-29(三) |
| 第 8 週 | Eligibility Traces 2023-04-05(三) |
| 第 9 週 | Planning and Learning with Tabular Methods Introduction 2023-04-12(三) |
| 第 10 週 | Approximate Solution Methods Introduction 2023-04-19(三) |
| 第 11 週 | On-policy Approximation of Action Values 2023-04-26(三) |
| 第 12 週 | Off-policy Approximation of Action Values 2023-05-03(三) |
| 第 13 週 | Policy Approximation 2023-05-10(三) |
| 第 14 週 | Psychology 2023-05-17(三) |
| 第 15 週 | Neuroscience 2023-05-24(三) |
| 第 16 週 | Applications and Case Studies 2023-05-31(三) |
| 第 17 週 | Final Project Presentation 2023-06-07(三) |
| 第 18 週 | Final Project Demonstration 2023-06-14(三) |
教師未提供此項資料
- 地點
- Go far 209
- 時間
- 10:00~12:00, Wed.
- 聯絡方式
- 03-5731350