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Updated in [August 18th, 2023]

Skills and Knowledge:
This course will provide students with the skills and knowledge to develop practical Reinforcement Learning algorithms in Python using PyTorch and PyTorch Lightning. Students will learn to implement adaptive algorithms that solve control tasks based on experience, as well as combine these techniques with Neural Networks and Deep Learning methods to create adaptive Artificial Intelligence agents. Additionally, students will gain an understanding of the Markov decision process (MDP), Q-Learning, Neural Networks, Deep Q-Learning, Policy Gradient methods, 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), and Hindsight Experience Replay (HER).
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, Neural Networks, Deep Q-Learning, Policy Gradient methods, PyTorch Lightning, Hyperparameter tuning with Optuna, Deep Q-Learning for continuous action spaces, Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Soft Actor-Critic (SAC), and Hindsight Experience Replay (HER). By learning these techniques, professionals can gain the skills necessary to develop adaptive Artificial Intelligence agents capable of solving decision-making tasks. This course will also prepare professionals for the next courses in this series, where they will explore other advanced methods that excel in other types of task.
Further Education:
Yes, this course is suitable for preparing further education. It covers a wide range of topics related to advanced reinforcement learning, from the Markov decision process (MDP) to Soft Actor-Critic (SAC). It also provides refresher modules on Q-Learning, Neural Networks, and Deep Q-Learning, as well as introducing students to PyTorch Lightning and Optuna for hyperparameter tuning. This course will provide students with the necessary knowledge and skills to pursue further education in the field of reinforcement learning.

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

Deep Q-Learning for continuous action spaces (Normalized Advantage Function)

Refresher: Policy gradient methods

Deep Deterministic Policy Gradient (DDPG)

Twin Delayed DDPG (TD3)

Soft Actor-Critic (SAC)

Hindsight Experience Replay

Final steps