Advanced Reinforcement Learning: policy gradient methods

这是 Udemy 上最完整的强化学习系列课程。在其中，您将学习使用 PyTorch 和 PyTorch 闪电在 Python 中实现一些最强大的深度强化学习算法。您将从头开始实施根据经验解决控制任务的自适应算法。您将学习将这些技术与神经网络和深度学习方法相结合，以创建能够解决决策任务的自适应人工智能代理。

本课程将向您介绍强化学习技术的最新技术。它还将为您准备本系列的下一个课程，我们将在其中探索在其他类型的任务中表现出色的其他高级方法。

该课程的重点是培养实践技能。因此，在学习了每个方法家族最重要的概念之后，我们将从头开始在 jupyter notebook 中实现它们的一种或多种算法。

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 48.0 KHz
Language: English | Size: 733 MB | Duration: 47 lectures • 2h 35m

Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: (REINFORCE, A2C, PPO, etc)

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, 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 Reinforcement Learning course series 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.

This course will introduce you to the state of the art in Reinforcement Learning techniques. It will also prepare you for the next courses in this series, where we will explore other advanced methods that excel in other types of task.

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.

Advanced Reinforcement Learning

– 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

– Rainbow Deep Q-Learning

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.