EXECUTIVE EDUCATION

Reinforcement Learning

Join MIT Professional Education's Live Virtual Program to Transform your Solutions

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Course Dates

STARTS ON

TBD

Course Duration

DURATION

6 weeks, live virtual
2 sessions/week

Course Duration

PROGRAM FEE

Course Information Special group enrollment pricing

Have You Registered? Limited Seats Available

This program is limited to a maximum of 75 enrollments only, early registrations are recommended to confirm participation.

Participant Profile

This program is ideally suited for technical professionals who wish to understand cutting-edge trends and advances in reinforcement learning. Professionals who wish to expand their knowledge regarding how to use RL in engineering and business settings will find this program particularly useful.

Prerequisites: To be able to take full advantage of this program, we recommend that participants have mathematical background in linear algebra and probability, basic knowledge of deep-learning and experience with programming (preferably Python).

This background will help participants follow some of the practical examples more effectively. There are two optional assignments in the program that will require a computer with Google CoLab that runs on any browser or Unix/Linux Terminal.

Representative job titles include:

  • Research Scientist
  • Machine Learning Engineer
  • Software Engineer
  • Data Scientist
  • Data Analyst
  • Business Analyst
  • Product Manager
  • Program Manager
  • CTO
Representative industries include:

  • Robotics
  • Automotive
  • Manufacturing
  • Urban Planning and Design
  • Logistics
  • Government & Military
  • Science & Technology
  • Retail
  • Healthcare & Pharmaceutical
  • Finance

Key Takeaways

  • Understand the basic principles of RL and learn when RL can be applied to your business problem and how to frame the problem for obtaining maximum gains from RL
  • Improve the performance of supervised learning systems by fine-tuning with RL methods
  • Understand how to use popular Deep RL algorithms such as DQN and PPO
  • Learn techniques for applying Deep RL methods to practical problems when it is impossible to collect large amounts of data

Program Topics

This live virtual program has 10 live sessions (two sessions per week). Each session is 90 minutes long.

Session 1:

Introduction to Decision Making and Why RL?

Session 2:

Basics of RL

Session 3:

Ingredients of Casting Your Problem into RL

Session 4:

Off Policy RL

Session 5:

On Policy RL

Session 6:

Problem Clinic 1

Session 7:

Problem Clinic 2

Session 8:

Improving Sample Efficiency of RL

Session 9:

Practical Challenges in Deep RL and How to Debug?

Session 10:

Frontiers of Research

Session 1:

Introduction to Decision Making and Why RL?

Session 6:

Problem Clinic 1

Session 2:

Basics of RL

Session 7:

Problem Clinic 2

Session 3:

Ingredients of Casting Your Problem into RL

Session 8:

Improving Sample Efficiency of RL

Session 4:

Off Policy RL

Session 9:

Practical Challenges in Deep RL and How to Debug?

Session 5:

On Policy RL

Session 10:

Frontiers of Research

Note: Sessions are held on Wednesdays and Fridays, 9:00 a.m.- 10:30 a.m ET. For full session schedule, please download the brochure.

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Live Virtual Experience Features

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Live RL Challenge Clinic

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Live Weekly Sessions with MIT Faculty

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Peer Discussion Learning Groups and Personalized Feedback

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Hands-on RL Implementation (optional assignments)

Participant Testimonials from Previous MIT Professional Education Courses

“The interaction and experience of both the speakers and the students was what made it one of the best courses I have taken. Even with the challenge of being virtual the course achieved beyond my expectations. I can only imagine how amazing this class would be in person.”

— James K., Retired Fire Chief

“I think this live virtual course was great. I felt I was in a classroom, and at the same time avoided all the hassle traveling and staying in hotels.”

— Cecile S., Works in U.S. Federal Government

“I was initially very disappointed that the class had to change format from in-person to Zoom due to Covid-19, however I was ultimately very happy I chose to participate as it proved to be an effective format.”

— Steve M., Director of Deployment

Program Faculty

Pulkit Agrawal

Steven G (1968) & Renee Finn CD Assistant Professor

Dr Pulkit Agrawal is an Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT and leads the Improbable AI Lab, part of the Computer Science and Artificial Intelligence Lab at MIT and affiliated with the Laboratory for Information and Decision Systems... More info

Cathy Wu

Gilbert W. Winslow (1937) Career Development Assistant Professor at MIT: LIDS, CEE, IDSS

Cathy Wu is the Gilbert W. Winslow (1937) Career Development Assistant Professor at MIT in the Laboratory for Information & Decision Systems (LIDS), the Department of Civil and Environmental Engineering (CEE), and the Institute for Data, Systems, & Society (IDSS). She holds a Ph.D. from UC Berkeley, and B.S. and M.Eng from MIT, all in EECS, and a Postdoc from Microsoft Research AI... More info

Pulkit Agrawal

Steven G (1968) & Renee Finn CD Assistant Professor

Dr Pulkit Agrawal is an Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT and leads the Improbable AI Lab, part of the Computer Science and Artificial Intelligence Lab at MIT and affiliated with the Laboratory for Information and Decision Systems. Dr. Agrawal cofounded SafelyYou, an organization that builds fall prevention technology, and the AI Foundry, an incubator for AI startups. Dr. Agrawal is recipient of Amazon Faculty Research Award, Sony Faculty Research Award, Salesforce Research Award, Signature Fellows Award and the Fulbright Science and Technology Award among others. His work has appeared multiple times in MIT Tech Review, Quanta, New Scientist, and the New York Post.

Cathy Wu

Gilbert W. Winslow (1937) Career Development Assistant Professor at MIT: LIDS, CEE, IDSS

Cathy Wu is the Gilbert W. Winslow (1937) Career Development Assistant Professor at MIT in the Laboratory for Information & Decision Systems (LIDS), the Department of Civil and Environmental Engineering (CEE), and the Institute for Data, Systems, & Society (IDSS). She holds a Ph.D. from UC Berkeley, and B.S. and M.Eng from MIT, all in EECS, and a Postdoc from Microsoft Research AI. She studies reliable decision making in the context of societal systems, and she draws from machine learning, optimization, control theory, and urban systems. Her recent research focuses on the computational challenges surrounding the integration of autonomy into existing urban systems. Cathy draws from prior experience and collaborations across fields and institutions, including Microsoft Research, Amazon, IBM, Taiwan's National Center for High Performance Computing, OpenAI, Google X's Self-Driving Car Team (now Waymo), AT&T, Caltrans, Facebook, and Dropbox. Her work has been acknowledged by several awards, including the 2019 IEEE ITSS Best Ph.D. Dissertation Award, 2019 Microsoft Location Summit Hall of Fame, 2018 Milton Pikarsky Memorial Dissertation Award, the 2016 IEEE ITSC Best Paper Award, and fellowships from NSF, Berkeley Chancellor, NDSEG, and Dwight David Eisenhower.

Certificate of Completion

Certificate of Completion

Get recognized! Upon successful completion of the program, MIT Professional Education provides certificate of completion to participants. This program is scored as a pass or no-pass; participants must attend 8 out of 10 Live Sessions to pass and obtain the certificate of completion.

This program may be taken individually or as part of the larger MIT Professional Education Certificate Program in Machine Learning and Artificial Intelligence. This professional certificate equips you with the best practices and actionable knowledge needed to put you and your organization at the forefront of the AI revolution.

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Note: Attendance will be tracked on Zoom. Participants should notify Program Support by email as early as possible in case of absence(s).
Participants are expected to watch the video recordings of any missed sessions.

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Early registrations are encouraged. Seats fill up quickly!