Miguel Morales is a reinforcement learning engineer at Lockheed Martin, Missiles and Fire Control, Autonomous Systems, in Denver, Colorado, where he applies reinforcement learning to autonomous systems. He is the author of Grokking Deep Reinforcement Learning, published by Manning, a book known for making one of the most mathematically demanding areas of machine learning intuitive and hands-on.

Miguel is also a part-time Instructional Associate at the Georgia Institute of Technology, where he teaches the graduate course in Reinforcement Learning and Decision Making. Earlier, he worked with Udacity as a reviewer and mentor, and later as a Content Developer for the Deep Reinforcement Learning Nanodegree, helping students around the world take their first steps in RL.

He graduated from Georgia Tech with a Master’s in Computer Science, specializing in Interactive Intelligence. Through his book, his teaching, and his openly shared course materials, Miguel focuses on demystifying reinforcement learning for engineers, students, and anyone curious about how agents learn from interaction.