Multi-agent Learning Seminar

Time: 

Mondays, 14h00 CoMo Lab (10G711)

[October 31: Important notification (by lecturer, Diederik M. Roijers):
Dear all,

The course will start Monday November 6, at the above-mentioned time and place. Until the end of the year (Nov 6 - Dec 18), there will be classes every Monday.

(After the Christmas holidays and study period in Januari, the course will then resume until the end of the academic year.)

Diederik M. Roijers (AI laboratory - VUB)
]

 
Prerequisites: 

Either Machine learning or Techniques of AI.

Description: 

This course will cover basic as well as advanced concepts of Reinforcement Learning

Introduction
Bandits
MDPs: dynamic programming
MDPs: Monte Carlo methodes
MDPs: temporal difference methods
MDPs: Model-based learning
Prioritised Sweeping
RL Learning Theory
Multi-agent learning: cooperative
Multi-agent learning: Markov Games
Multi-objective planning/RL
Deep Reinforcement Learning: DQL en double DQL
Deep Reinforcement Learning: A3C

Examination: 

Students will be evaluated based on following criteria:

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

 

Material: 

 

Basic Reinforcement Learning book:  

Reinforcement Learning: An Introduction

 

Software:

  • 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.