Reinforcement Learning Explained

Learn how to frame reinforcement learning problems, tackle classic examples, explore basic algorithms from dynamic programming, temporal difference learning, and progress towards larger state space using function approximation and DQN (Deep Q Network).

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Created by: Kenneth Tran

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Course Description

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.
edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.

Advanced Level

Time: 6 Weeks - 4-8 hours per week

Instructor Details

Kenneth Tran

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Kenneth is a Principal Research Engineer at the Deep Learning Technology Center. He has wide interest in Machine Learning spanning from optimization algorithms to distributed systems. His current main research pursuit is deep reinforcement learning with focus on off-policy learning and sample efficient methods, safe exploration, reverse reinforcement learning and real-world optimal control applications, including drones control, data center energy optimization, indoor farming optimization, etc.