Introduction to Reinforcement Learning (UTN 2026)

Reinforcement Learning (RL) is an area of Machine Learning that has recently made large advances and has been publicly visible by reaching and surpassing human skill levels in games like Go and Starcraft. These successes show that RL has the potential to transform many areas of research and industry by automatizing the development of processes that once needed to be engineered explicitly.

In contrast to other machine learning paradigms, which require the presence of (labeled or unlabeled) data, RL considers an agent that takes actions in an environment and learns from resulting feedback. The agent maximizes a reward signal that it receives for desirable outcomes, while at the same time trying to explore the world in which it operates to find yet unknown, potentially more rewarding action sequences–a dilemma known as the exploration-exploitation tradeoff. Recent advances in machine learning based on deep learning have made RL methods particularly powerful since they allow for agents with particularly well performing models of the world.

Course Material

  1. Introduction to Reinforcement Learning (pdf)
  2. Dynamic Programming (CV)
  3. Model-Free Prediction (CV)
  4. Model-Free Control I (Interactive HTML lecture)
  5. Model-Free Control II
  6. Policy Gradient Methods I (CV)
  7. Policy Gradient Methods II (CV)
  8. Policy Gradient Methods III (CV)
  9. Model-Based Reinforcement Learning I
  10. Model-Based Reinforcement Learning II
  11. Introduction to Exploration (CV)
  12. Exploration in Deep RL and Modern RL Theory Questions (CV)

Lectures marked (CV) are taught by Claire Vernade. Her course website can be found here.

Literature

  • Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA. [link]
  • Bellman, R.E. 1957. Dynamic Programming. Princeton University Press, Princeton, NJ. Republished 2003: Dover, ISBN 0-486-42809-5.
  • UC Berkeley CS188: Intro to AI [link]
  • University College London Course on RL [link]
  • Advanced Deep Learning and Reinforcement Learning (UCL + DeepMind) [link]
  • https://cs.stanford.edu/people/karpathy/reinforcejs/gridworld_dp.html
  • https://cs.stanford.edu/people/karpathy/reinforcejs/gridworld_td.html

Interesting talks, articles, and blog-posts:

  • Joelle Pineau: Reproducible, Reusable, and Robust Reinforcement Learning [youtube]
  • David Silver: AlphaGo, AlphaZero, and Deep Reinforcement Learning | AI Podcast #86 with Lex Fridman [youtube]
  • Technion Research: Why does reinforcement learning not work (for you)? [link]
  • RL algorithms quick overview [link]

Code examples and exercises:

  • GitHub Repo of Denny Britz: https://github.com/dennybritz/reinforcement-learning/tree/master/DP