Udemy - Advanced Reinforcement Learning in Python - from DQN to SAC torrent

Advanced Reinforcement Learning in Python: from DQN to SAC



https://DevCourseWeb.com

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.41 GB | Duration: 8h 5m

Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: DDPG, TD3, SAC, NAF, HER.

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).
Know basic statistics (mean, variance, normal distribution)
Description
This is the most complete Advanced Reinforcement Learning course on Udemy. In it, you will learn to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will implement from scratch adaptive algorithms that solve control tasks based on experience. 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.