Multi-agent Learning Seminar



Lectures will take place every other Monday, 14:00-16:00 at Pleinlaan 9, 3rd floor, room 3.31. 




Either Machine learning or Techniques of AI.

You should be able to understand gradient descent and backpropagation, basic ML concepts on regression and classification, loss functions, linear and non-linear models, test error and overfitting etc. You can follow the extra resources provided in this page for a refresher.



Despite the "multi-agent" title, the course will serve as an introduction to the basic concepts of single agent RL and slowly build up to advanced concepts leading to current state-of-the-art methods and Deep RL.



  • Introduction and course Overview
  • Markov Decision Processes
  • Dynamic Programming
  • Value Function Approximation
  • Policy Gradients
  • Exploration Exploitation
  • Detour: Deep Learning, Neural Nets and Convnets
  • Deep RL: Value Based
  • Deep RL: Policy Based
  • The future and open problems



Students will be evaluated based on following criteria:

  • Course participation (each student will have to hand in solutions to at least 2 course exercises + participation in discussions)
  • Learning agent project (2nd semester)
  • Project defense & report (2nd semester exam period)



Class dates for 1st Semester:

  • 29/10/2018
  • 12/11/2018
  • 26/11/2018
  • 10/12/2018


Class dates for 2nd Semester:

  • 18/02/2019
  • 04/03/2019
  • 18/03/2019
  • 01/04/2019
  • 29/04/2019
  • 13/05/2019




Basic Reinforcement Learning book:  

Reinforcement Learning: An Introduction 2nd edition

Other resources

For neural networks material


  • The OpenAI Gym will be use as a testbed for learning algorithms (if you do not program your own environment, which is also allowed).
  • Experiments will be coded in Python. We recommend installation of the Anaconda framework to get all the scientific computing libraries.
  • User manual to use the project starter code on the hydra cluster.