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Reinforcement Learning beginner to master - AI in Python
12,928 students
What you'll learn
- Understand the Reinforcement Learning paradigm and the tasks that it's best suited to solve.
- Understand the process of solving a cognitive task using Reinforcement Learning
- Understand the different approaches to solving a task using Reinforcement Learning and choose the most fitting
- Implement Reinforcement Learning algorithms completely from scratch
- Fundamentally understand the learning process for each algorithm
- Debug and extend the algorithms presented
- Understand and implement new algorithms from research papers
Explore related topics
Course content
13 sections • 134 lectures • 10h 46m total length
Requirements
- Be comfortable programming in Python
- Know basic linear algebra and calculus (matrices, vectors, determinants, derivatives, etc.)
- Know basic statistics and probability theory (mean, variance, normal distribution, etc.)
Description
This is the most complete Reinforcement Learning course on Udemy. In it you will learn the basics of Reinforcement Learning, one of the three paradigms of modern artificial intelligence. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will also learn to combine these algorithms with Deep Learning techniques and neural networks, giving rise to the branch known as Deep Reinforcement Learning.
This course will give you the foundation you need to be able to understand new algorithms as they emerge. It will also prepare you for the next courses in this series, in which we will go much deeper into different branches of Reinforcement Learning and look at some of the more advanced algorithms that exist.
The course is focused on developing practical skills. Therefore, after learning the most important concepts of each family of methods, we will implement one or more of their algorithms in jupyter notebooks, from scratch.
This course is divided into three parts and covers the following topics:
Part 1 (Tabular methods):
- Markov decision process
- Dynamic programming
- Monte Carlo methods
- Time difference methods (SARSA, Q-Learning)
- N-step bootstrapping
Part 2 (Continuous state spaces):
- State aggregation
- Tile Coding
Part 3 (Deep Reinforcement Learning):
- Deep SARSA
- Deep Q-Learning
- REINFORCE
- Advantage Actor-Critic / A2C (Advantage Actor-Critic / A2C method)
Who this course is for:
- Developers who want to get a job in Machine Learning
- Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.
- Researchers/scholars seeking to enhance their practical coding skills.