Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
5 Hour(s) 13 Minute(s)
Language
English
Taught by
Holczer Balazs
Rating
2 views
Course Overview
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
Course Content
- 10 section(s)
- 50 lecture(s)
- Section 1 Introduction
- Section 2 Artificial Intelligence Basics
- Section 3 Markov Decision Process (MDP) Theory
- Section 4 Exploration vs. Exploitation Problem
- Section 5 Q Learning Theory
- Section 6 Q Learning Implementation
- Section 7 Deep Reinforcement Learning Theory
- Section 8 Deep Q Learning Implementation
- Section 9 Proximal Policy Optimization (PPO) Theory
- Section 10 Course Materials (DOWNLOADS)
What You’ll Learn
- Understand reinforcement learning
- Understand Markov Decision Processes
- Understand value- and policy-iteration
- Understand Q-learning approach and it's applications
Reviews
-
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.
