Reinforcement Learning Course - Full Machine Learning Tutorial

Reinforcement learning is an area of machine learning that involves taking right action to maximize reward in a particular situation. In this full tutorial course, you will get a solid foundation in reinforcement learning core topics. The course covers Q learning, SARSA, double Q learning, deep Q learning, and policy gradient methods. These algorithms are employed in a number of environments from the open AI gym, including space invaders, breakout, and others. The deep learning portion uses Tensorflow and PyTorch. The course begins with more modern algorithms, such as deep q learning and policy gradient methods, and demonstrates the power of reinforcement learning. Then the course teaches some of the fundamental concepts that power all reinforcement learning algorithms. These are illustrated by coding up some algorithms that predate deep learning, but are still foundational to the cutting edge. These are studied in some of the more traditional environments from the OpenAI gym, like the cart pole problem. 💻Code:https://www.youtube.com/redirect?v=ELE2_Mftqoc&redir_token=FGUbxLwsJ9XqN9RpUlgWsrI2DGx8MTU4NTIyMTIyOEAxNTg1MTM0ODI4&event=video_description&q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository%2Ftree%2Fmaster%2FReinforcementLearning Course from Machine Learning with Phil. Check out his YouTube channel: https://www.youtube.com/channel/UC58v9cLitc8VaCjrcKyAbrw -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp