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Keep shoppingAdvanced Reinforcement Learning in Python: cutting-edge DQNs
1,771 students
Advanced Reinforcement Learning in Python: cutting-edge DQNs
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: From basic DQN to Rainbow DQN
Created byJavier Ventajas
Last updated 5/2025
English
What you'll learn
- Master some of the most advanced Reinforcement Learning algorithms.
- Learn how to create AIs that can act in a complex environment to achieve their goals.
- Create from scratch advanced Reinforcement Learning agents using Python's most popular tools (PyTorch Lightning, OpenAI gym, Optuna)
- Learn how to perform hyperparameter tuning (Choosing the best experimental conditions for our AI to learn)
- 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
14 sections • 104 lectures • 8h 26m total length
Requirements
- 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)
Description
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.
Leveling modules:
- Refresher: The Markov decision process (MDP).
- Refresher: Q-Learning.
- Refresher: Brief introduction to Neural Networks.
- Refresher: Deep Q-Learning.
Advanced Reinforcement Learning:
- PyTorch Lightning.
- Hyperparameter tuning with Optuna.
- Reinforcement Learning with image inputs
- Double Deep Q-Learning
- Dueling Deep Q-Networks
- Prioritized Experience Replay (PER)
- Distributional Deep Q-Networks
- Noisy Deep Q-Networks
- N-step Deep Q-Learning
- Rainbow Deep Q-Learning
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.