Advanced Reinforcement Learning in Python: cutting-edge DQNs

4.8

Learners: 891

Instructor: Escape Velocity Labs

Duration:

This Advanced Reinforcement Learning course on Udemy is the most comprehensive of its kind. It will teach you to implement powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will learn to create adaptive Artificial Intelligence agents that can solve decision-making tasks, combining Reinforcement Learning techniques with Neural Networks and Deep Learning methods. This course will also prepare you for the next courses in the series, where you will explore other advanced methods. With practical skills in mind, you will implement algorithms from scratch in jupyter notebooks. Don't miss out on this opportunity to learn the state of the art in Reinforcement Learning! ▼▲

Course Feature Course Overview Course Provider Discussion and Reviews

Go to class

Course Feature

Cost:

Paid

Provider:

Udemy

Certificate:

Paid Certification

Language:

English

Start Date:

2022-12-29

Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [August 18th, 2023]

Skills and Knowledge:
By taking this course, you will acquire the skills and knowledge to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. 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. Additionally, you will gain an understanding of the Markov decision process (MDP), Q-Learning, and Neural Networks. You will also learn about advanced Reinforcement Learning techniques such as 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, and Rainbow Deep Q-Learning.
Professional Growth:
This course provides a comprehensive introduction to the field of Advanced Reinforcement Learning. It covers the fundamentals of the Markov decision process (MDP), Q-Learning, and Neural Networks, as well as more advanced topics such as 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, and Rainbow Deep Q-Learning. By providing a comprehensive overview of the field, this course will help professionals gain the skills and knowledge necessary to develop and implement cutting-edge Reinforcement Learning algorithms.
Further Education:
This course is suitable for preparing further education in the field of Reinforcement Learning. It covers the most important concepts and algorithms in the field, and provides practical skills in implementing them from scratch. Additionally, the course introduces advanced techniques such as Hyperparameter tuning with Optuna, Reinforcement Learning with image inputs, and various Deep Q-Learning algorithms. This makes it an ideal course for those looking to gain a comprehensive understanding of Reinforcement Learning and prepare for further education in the field.