Reinforcement Learning Explained

Course details

Reinforcement Learning (RL) is an area of machine learning, where an agent learns by interacting with its environment to achieve a goal.  In this course, you will be introduced to the world of  reinforcement learning. You will learn how to frame reinforcement learning problems and start tackling classic examples like news recommendation, learning to navigate in a grid-world, and balancing a cart-pole. You will explore the basic algorithms from multi-armed bandits, dynamic programming, TD (temporal difference) learning, and progress towards larger state space using function approximation, in particular using deep learning. You will also learn about algorithms that focus on searching the best policy with policy gradient and actor critic methods. Along the way, you will get introduced to Project Malmo, a platform for Artificial Intelligence experimentation and research built on top of the Minecraft game.Release schedule:This course is of rolling release model, there are 7 modules in this course, M00 and M01 are released when the course is live, other modules will be released according to the following schedule: Dec 18: Course live: module 0 and 1 Jan 1: Happy New Year! Module 3 live Jan 15: Module 4 live Jan 22: Module 5 live Jan 29: Module 6 live Feb 5: Module 7 live Updated on 17 September, 2019