This is the most complete Advanced Reinforcement Learning course on Udemy. In it, you will learn to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will learn to combine these techniques with Neural Networks and Deep Learning methods to create adaptive Artificial Intelligence agents capable of solving decision-making tasks.
This course will introduce you to the state of the art in Reinforcement Learning techniques. It will also prepare you for the next courses in this series, where we will explore other advanced methods that excel in other types of task.
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.
– Refresher: The Markov decision process (MDP).
– Refresher: Q-Learning.
– Refresher: Brief introduction to Neural Networks.
– Refresher: Deep Q-Learning.
– Refresher: Policy gradient methods
Advanced Reinforcement Learning:
– PyTorch Lightning.
– Hyperparameter tuning with Optuna.
– Deep Q-Learning for continuous action spaces (Normalized advantage function – NAF).
– Deep Deterministic Policy Gradient (DDPG).
– Twin Delayed DDPG (TD3).
– Soft Actor-Critic (SAC).
– Hindsight Experience Replay (HER).
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.
- Robotics students and researchers.
- Engineering students and researchers.
- Be comfortable programming in Python
- Completing our course “Reinforcement Learning beginner to master” or being familiar with the basics of Reinforcement Learning (or watching the leveling sections included in this course).
- Know basic statistics (mean, variance, normal distribution)
Last Updated 4/2022