Best Reinforcement Learning Tutorials, Examples, Projects, and Courses. In this list, you’ll find: reinforcement learning tutorials, examples of where to apply reinforcement learning, interesting reinforcement learning projects, courses to master reinforcement learning.
Reinforcement Learning (RL): A high-level structural overview of classical Reinforcement Learning algorithms. Reinforcement Learning is learning what to do — how to map situations to actions — so as to maximize a numerical reward signal.
Current algorithms lean more towards narrow learning, meaning they are good at learning and solving specific types of problems under specific conditions.
In this blog on Applications of Reinforcement Learning, you will learn about real world reinforcement learning applications & examples in robotics, marketing, healthcare & finance.
Python Machine Learning, Third Edition covers the essential concepts of reinforcement learning, starting from its foundations, and how RL can support decision making in complex environments. Read more on the topic from the book's author Sebastian Raschka.
In Reinforcement Learning (RL), agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves and punished for the wrong ones.
Since that renowned conference at Dartmouth College in 1956, AI research has experienced many crests and troughs of progress through the years. From the many lessons learned during this time, some have needed to be re-learned -- repeatedly -- and the most important of which has also been the most difficult to accept by many researchers.
The future and promise of DRL are therefore bright and shiny. In this article, we touched upon the basics of RL and DRL to give the readers a flavor of this powerful sub-field of AI.