Download Advanced Reinforcement Learning in Python: from DQN to SAC

What you would learn in Advanced Reinforcement Learning in Python: from DQN to SAC course?

This is the complete Advanced Reinforcement Learning course on Udemy. In it, you'll learn how to implement the most effective Deep Reinforcement Learning algorithms in Python by using PyTorch and PyTorch lightning. Then, you will create the creation of adaptive algorithms to resolve control problems using experiences. You will be taught to integrate these methods using Neural Networks and Deep Learning methods to create adaptable Artificial Intelligence agents capable of taking on decision-making tasks.

In this course, you will be introduced to the latest developments in Reinforcement Learning methods. The course also helps prepare for the following courses in the series, in which we will examine other innovative techniques that are effective in different kinds of tasks.

The class is focused on learning practical abilities. Thus, after learning the key aspects of each family of methods, we'll apply any of them to Jupyter notebooks starting from scratch.

Modules for leveling:

- Refresher: The Markov decision process (MDP).

- Refresher: Q-Learning.

- Refresher: Brief introduction to Neural Networks.

- Refresher: Deep Q-Learning.

- Refresher: Policy gradient methods

Advanced Reinforcement Learning:

- PyTorch Lightning.

- 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).

- Hindsight Experience Replay (HER).

Course Content:

Be a master of some of the most sophisticated Reinforcement Learning techniques.
Learn to build AIs that operate in complex environments to meet their goals.
Create entirely from scratch the most advanced Reinforcement Learning agents with Python's most widely used software (PyTorch Lightning OpenAI gym Brax, Optuna)
Learn how to do hyperparameter tuning (Choosing the ideal environment for AIs to study)
Understand the fundamental learning process for each algorithm.
Expand and debug the algorithms described.
Learn and apply new algorithms that are derived from research papers.