Multi-agent Learning Seminar

Time: 

Mondays, 14h00 CoMo Lab (10G711)

 
Assistant: 
Prerequisites: 

Prior experience with reinforcement learning / machine learning such as from the Machine Learning, Techniques of AI or Computational Game Theory courses is recommended.

Description: 

In this course we will look at different learning algorithms for intelligent agents. After an initial introduction to some of the more avanced learning methods students will be  able to select a research project to work on. The focus of these projects will be on applying learing methods in robotics or large simulated problems.

 

 

This year the course will focus on applying learning in the OpenAI gym, a community driven AI benchmark suite. The sessions will take the form of regular (but not weekly) workshops. After the initial introduction to reinforcement learning, the students will be required to independently read research papers introducing various more advanced techniques. At the workshop one student will be tasked with presenting the paper, after which it will be discussed in group. Students will then implement and test the technique in a benchmark problem by the next session.

Examination: 

Students will be evaluated based on following criteria:

  • Course participation (presentation of paper + participation in discussions)
  • Learning agent project (2nd semester)
  • Project defense & report (2nd semester exam period)

 

Schedule: 
Material: 

 

Basic Reinforcement Learning book:  

Reinforcement Learning: An Introduction

 

Examples of Papers to read:

Software:

  • The OpenAI Gym will be use as a testbed for learning algorithms
  • Experiments will be coded in Python. We recommend installation of the Anaconda framework to get all the scientific computing libraries.