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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.

Course Syllabus

Introduction

Refresher: The Markov Decision Process (MDP)

Refresher: Q-Learning

Refresher: Brief introduction to Neural Networks

Refresher: Deep Q-Learning

PyTorch Lightning

Hyperparameter tuning with Optuna

Double Deep Q-Learning

Dueling Deep Q-Networks

Prioritized Experience Replay

Noisy Deep Q-Networks

N-step Deep Q-Learning

Distributional Deep Q-Networks

Final steps