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

4.86 (73 reviews)
Udemy
platform
English
language
Data Science
category
Advanced Reinforcement Learning in Python: cutting-edge DQNs
1,282
students
8.5 hours
content
Jan 2024
last update
$69.99
regular price

What you will 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.

Why take this course?

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

Screenshots

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Reviews

William
December 11, 2022
This has been a good review of what I learned previously and I am looking forward to learning more advanced methods.
Richard
August 3, 2022
This course has really helped me better understand reinforcement learning, and enjoy how we use these algorithms for tons of applications. I would recommend these courses to anyone who wants to learn more about reinforcement learning to apply into their own field.
Jonattan
May 14, 2022
Very good content, demonstrates a lot of methods and tools including theory and practical codes. I bought courses from different authors and this is the best series about reinforcement learning in my opinion. Part of this course is copied as a refresher from other courses of this series.

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4126942
udemy ID
6/16/2021
course created date
4/21/2022
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