Udemy – Introduction to Reinforcement Learning (RL) 2024-11

Published on: 2024-12-09 22:38:41

Categories: 28

Description

Introduction to Reinforcement Learning (RL) course. This course introduces you to the fascinating world of Deep Reinforcement Learning. This comprehensive and practical course is designed for beginners and enthusiasts who want to fully master reinforcement learning techniques with PyTorch. With no specific prerequisites, we cover fundamental concepts—including value functions, value-action functions, and the Bellman equation—to give you a solid theoretical foundation.

What you will learn in this course:

Atari Games with Deep Reinforcement Learning: Discover how reinforcement learning agents learn classic Atari games and understand the groundbreaking concepts behind the first wave of deep Q learning.
Human-level control with deep reinforcement learning: Take a closer look at how deep Q-networks (DQNs) achieved human-level performance and revolutionized the field of reinforcement learning.
Asynchronous Methods for Deep Reinforcement Learning: Explore asynchronous advantage actor-critic (A3C) methods that improved both stability and performance in reinforcement learning, allowing agents to learn faster and more effectively.
Approximate Policy Optimization (PPO) Algorithms: Learn PPO in depth, one of the most powerful and efficient algorithms used in advanced reinforcement learning research and applications.

Who is this course for?

This course is rich in hands-on coding sessions where you will implement each algorithm from scratch with PyTorch. By the end, you will have a project portfolio and a solid understanding of both the theory and practice of deep reinforcement learning.

What you will learn in the Introduction to Reinforcement Learning (RL) course

Main concepts of reinforcement learning
Implementing reinforcement learning algorithms in PyTorch
Building agents to play Atari games
Exploring policy-based and value-based approaches
Mastering exploration versus exploitation

This course is suitable for people who:

Artificial Intelligence Researchers and Academics
Game developers and simulation engineers
Graduate students in artificial intelligence and machine learning
Data scientists and machine learning engineers
Beginners interested in machine learning
Software developers looking to explore artificial intelligence

Note: Persian equivalents for specialized words have not been used to provide a deeper understanding of the concepts for the Persian-speaking audience.