Reinforcement Learning Study Group

Community Mentors

Anna MachensAnna Machens
This community is guided by Anna Machens. For additional details on joining, meeting schedules, and any other inquiries, please reach out to them directly.

Welcome to our professional learning community dedicated to exploring the exciting field of reinforcement learning (RL). Whether you’re a seasoned academic or just beginning your journey, we’re delighted to have you join us.

What Is Reinforcement Learning?

Reinforcement learning focuses on how agents learn to make decisions by interacting with their environment. Unlike supervised learning, where explicit labels guide training, RL agents learn through trial and error, aiming to maximize cumulative rewards. It’s akin to teaching a dog new tricks, but with algorithms!

Core Resources

  1. Reinforcement Learning: An Introduction (Second Edition)
    • Our foundational text is the second edition of “Reinforcement Learning: An Introduction” by Richard S. Sutton and Andrew G. Barto. This comprehensive book provides clear insights into RL’s key ideas and algorithms.
  2. Google DeepMind’s Lecture Series on YouTube
    • DeepMind’s lecture series covers everything from RL basics to cutting-edge research. These videos complement our reading material and offer practical perspectives.

Community Meetings

We meet every two weeks, alternating between Tuesday afternoons and Thursday lunchtime sessions. During these gatherings, we delve into RL concepts, discuss recent research, and share insights. Expect engaging discussions and thought-provoking questions.

Who Should Join?

Our community is open to students, staff, researchers, and teachers affiliated with the University of Twente. If you’re part of this vibrant academic ecosystem, we invite you to join us! Whether you’re a seasoned researcher or a curious student, our RL discussions will enrich your understanding.

If you’ve dabbled in machine learning (ML) but haven’t explored RL extensively, this community is perfect for you. We’ll bridge the gap between ML and RL, demystifying algorithms and techniques.

Participation requires dedication—approximately two hours per week for homework, reading, and attending sessions. Our friendly atmosphere and collaborative spirit will make the journey enjoyable.

So, pack your lunch, fire up your curiosity, and let’s explore the world of reinforcement learning together!