Advanced Reinforcement Learning in Python: from DQN to SAC

Advanced Reinforcement Learning in Python: from DQN to SAC
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

What you’ll 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, Brax, 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.

Requirements
Be comfortable programming in Python
Completing our course "Reinforcement Learning beginner to master" or being familiar with the basics of Reinforcement Learning (or watching the leveling sections included in this course).
Description
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.

– 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).

Who this course is for
Developers who want to get a job in Machine Learning.
Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.
Robotics students and researchers.
Engineering students and researchers.