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課程資料
報名日期*
全年招生
課程級別
學習模式
修業期
5 小時 13 分鐘
教學語言
英語
授課導師
Holczer Balazs
評分
*提示: 以上時間表僅供參考，以機構網站為準。
課程簡介
All you need to know about Markov Decision processes, value- and policy-iteation as well as about Q learning approach
This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment. So these are the topics:
- Markov Decision Processes
- value-iteration and policy-iteration
- Q-learning fundamentals
- pathfinding algorithms with Q-learning
- Q-learning with neural networks
課程章節
- 10 個章節
- 50 堂課
- 第 1 章 Introduction
- 第 2 章 Artificial Intelligence Basics
- 第 3 章 Markov Decision Process (MDP) Theory
- 第 4 章 Exploration vs. Exploitation Problem
- 第 5 章 Q Learning Theory
- 第 6 章 Q Learning Implementation
- 第 7 章 Deep Reinforcement Learning Theory
- 第 8 章 Deep Q Learning Implementation
- 第 9 章 Proximal Policy Optimization (PPO) Theory
- 第 10 章 Course Materials (DOWNLOADS)
課程內容
- Understand reinforcement learning
- Understand Markov Decision Processes
- Understand value- and policy-iteration
- Understand Q-learning approach and it's applications
評價
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GGuilherme Alves Silveira
excellent course, well made snd explained!
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MMark Smeets
I had liked some java example of deep reinforcement learning
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JJohanholtman
This course was going in very great detail. Not easy. But thorough. Thank you for providing this series of 4 courses.
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PPawel Jasinski
It's a really good course. I didn't realize that reinforcement learning is such powerful, especially when you combine it with deep learning.
