Advanced Reinforcement Learning

Explore the cutting-edge of RL research

Reinforcement learning (RL) is transforming machine learning applications across industries—and its potential is only beginning to be tapped. From natural language processing and computer vision to self-driving cars and gaming, this paradigm offers practical applications in industries as diverse as transportation, retail, finance, urban planning, and healthcare.

In this accelerated course, you’ll receive an advanced overview of the cutting-edge RL topics that are driving exciting advancements in machine learning. Through interactive lectures and exercises, you’ll acquire a multi-faceted glimpse into the development and potential of RL, from the perspectives of statistics, optimal control, economics, operational research, and other disciplines.

You will additionally have the opportunity to put your learning into practice during hands-on clinics, in which you will use advanced algorithms to solve real-world problems, and then discuss your solutions with the class and instructors during office hours. You will leave the course armed with a broad understanding of reinforcement learning as a tool, mathematical framework, and active field of study.

Certificate of Completion from MIT Professional Education

A majority of the course will be dedicated to deep overviews into key topics in active research, including offline reinforcement learning, the theory of RL, multi-agent RL, Monte Carlo Tree Search, hierarchical RL, and model-based RL exploration. Additional sessions will focus on practical considerations when using deep RL methods, such as deep learning architectures, and what actually makes deep RL methods work.

By completing this course, you will enhance your ability to:

• Determine the reinforcement learning framework (e.g. goal-directed, hierarchical, offline reinforcement learning, bandits) that is best-suited to solve a specific problem
• Select the most promising algorithms for an already-formulated reinforcement learning problem
• Recognize the limitations of reinforcement learning in order to judge whether a situation is suited for these strategies

Links & Resources

• A four-legged robotic system for playing soccer on various terrains, MIT News, April 3, 2023
• Is Reinforcement Learning Right for Your AI Problem?, Enterprise AI, July 9, 2021

This course is designed for mid-career professionals who are actively involved in or want to learn more about reinforcement learning. The strategies covered will be applicable for a wide variety of fields, including robotics, automotive, manufacturing, urban planning and design, logistics, government and military, science and technology, retail, finance, healthcare, and pharmaceutical industries.The curriculum will be particularly helpful for:

• Research scientists looking to enhance their ability to leverage advanced deep RL tools, such as multi-agent RL or Monte Carlo Tree Search
• Data scientists who are already familiar with RL strategies, but want to take their machine learning toolkit to the next level
• Data analysts and business analysts who are responsible for solving challenges that require extracting insights from large sets of unstructured data
• Machine learning engineers and software engineers who want to use advanced RL models to overcome practical constraints in existing projects
• Product managers and program managers who need to discern when it’s appropriate and effective to apply RL
• CTOs and other technology leaders who want a deeper understanding of how advanced RL strategies can 

REQUIREMENTS

Participants should be familiar with the basics of RL, including exact dynamic programming algorithms, Q-learning, deep neural networks, machine learning libraries (e.g. PyTorch or Tensorflow), and basic deep RL methods (DQN, policy gradient methods).