The Best Resources to Learn Reinforcement Learning
- Tech news
- July 27, 2023
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Introduction
It’s exciting how RL involves agents acquiring knowledge about their surroundings through feedback loops in order to accomplish some end goal. Rather than using training data provided by a human, a reinforcement learner actively takes action and receives feedback in the form of rewards and/or punishments. As time progresses, this dynamic interaction will help the agent improve its performance continuously. Numerous industries, such as those that involve language processing, robots, data analytics, and digital currencies, have adopted AI methods.
New RL advancements like chatGPT can enable humanoid AIs. As RL progresses, more specialists and investigators will require expertise on it. Mounds of useful online resources are easily accessible to assist you in starting your career. We will discuss many excellent resources for training in reinforcement learning
Online Courses
Reinforcement Learning Specialization — by Coursera
Coursera’s Reinforcement Learning Specialization from the University of Alberta and the Alberta Machine Intelligence Institute provides an extensive overview of the core concepts of RL. Included with the coursework is an RL Capstone Project. To understand the concepts in depth, students will undergo handson assignments
Reinforcement Learning Lectures 2021 - By DeepMind x UCL
From basic principles to cutting edge techniques in RL, this course covers all of it. Reputed academicians and researchers from DeepMun and UCL provide these lectures for this RL-oriented course.
Stanford CS234: Reinforcement Learning — Winter 2019
Prof. Emma Brunskill is teaching this comprehensive Stanford course on MDPs, Monte Carlo methods, and deep RL. These video lectures aim to cater to machine-learning enthusiasts.
David Silver gives a introduction into Reinforcement Learning
Professor David Silver and his famous class offer the ideal introduction into RL. A leading expert on RL, Professor Silver, has researched several related areas including dynamic programming, TD-learning and deep learning
UC Berkeley CS 285: Deep Reinforcement Learning — Fall 2021
The course CS 285 offered at UC Berkeley deals with deep RL. Professor Sergey Levine’s course delved into Markov decision processes, deep Q-learning, and policy gradient methods.
Books and Extra Resources
“Reinforcement Learning: An Introduction” by Richard S. Sutton and Andrew G. Barto
Referred to as the ‘bible’ of RL, this book is considered comprehensive and thorough.
Written by Pieter Abbeel, John Schulman, and John Tobin in their book “Deep Reinforcement Learning”
This book introduces deep reinforcement learning to the reader.
Deep RL Course by UC Berkeley
This training provides in-depth RL knowledge.
Spinning up in deep reinforcement learning by Open AI
This site provides detailed instructions on implementing RL techniques utilizing OpenAI Baselines.
Conclusion
Rewarding learning involves rewarding agents after taking action. Experienced programmers and amateurs alike can discover the required learning tools through these mentioned resources Ranging from online course to textbooks and further learning material, you have a range of tools available to help you master reinforcement learning. Your voyage through RL is set to begin; it offers an unlimited number of thrilling opportunities for adventure.
