Hi, my name is Miguel. I’m a software engineer for Lockheed Martin working on autonomous systems. Lockheed Martin is a global security in aerospace company engaged in the production of advanced technology systems. The majority of our business is naturally, with the US Department of Defense. At autonomous systems in Littleton, Colorado, we do all things autonomy, from traditional robotics to machine learning. In recent years, we have been working on developing high levels of autonomy in complex missions, and environments. We’ve applied deep learning techniques, including deep reinforcement learning to real-world military applications. I’m also a part-time instructional associate at Georgia Institute of Technology. At Georgia Tech, I have the privilege of guiding hundreds of graduate students every semester through their reinforcement learning of decision-making course, co-taught by Professor Charles Isbell and Professor Michael Littman. In a special teaching sessions, I’ve had the privilege to host several prominent contributors to the reinforcement learning community, including Professor Rich Sutton and Professor Leslie Caubain. Teaching at Tech has been one of the most rewarding jobs I’ve had. Finally, I’m the author of the book Grokking Deep Reinforcement Learning by Manning Publications. Writing a book is unlike anything I have done before. Teaching in a static medium such as paper, and to students of diverse backgrounds poses great challenges, but it has also taught me many things that I intend to apply to these lessons. In this module, I’ll be guiding you through one of the most important classes of algorithms to date, Actor-Critic Methods. Let’s get started.