Climbing the Ladder: Reinforcement Learning in a Competitive Setting

09:00-17:00, January 26

Workshop / Overview

Participants will get hands-on experience with modern reinforcement learning methods, building agents that will compete against one another.

In the morning, we will introduce the main reinforcement learning approaches. Participants will get familiar with them by solving toy problems.

In the afternoon, participants will design their own agent, which will then interact with other people’s agents in a (friendly) competitive setting.

Workshop / Outcome

Participants will discover what reinforcement learning is, what it can do, and what are its current limitations and perspectives. They will get hands-on experience by building and tweaking agents in a competitive setting. 

Workshop / Difficulty

Intermediate level

Workshop / Prerequisites

  • Each participant is expected to actively take part by designing and training RL agents on their machine.
  • Participants can also form small teams, working on the same laptop on a single agent.
  • A recent laptop is recommended, but a GPU is not required.
  • Participants should have a good knowledge of Python, and at least a basic understanding of machine learning.
  • No knowledge of reinforcement learning is expected. We will use the PyTorch framework, but we don’t expect participants to be familiar with it.

Track / Co-organizers

Frederic Ouwehand

Course Developer, EPFL Extension School

Florian Laurent

ML Engineer, AIcrowd

Christian Scheller

Research assistant, Master student, FHNW

AMLD EPFL 2020 / Workshops

A Conceptual Introduction to Reinforcement Learning

With Kevin Smeyers, Katrien Van Meulder & Bram Vandendriessche

09:00-12:30 January 25

Applied Machine Learning with R

With Dirk Wulff, Markus Steiner & Michael Schulte-Mecklenbeck

09:00-17:00 January 25

Augmenting the Web browsing experience using machine learning

With Oleksandr Paraska, Vasily Kuznetsov, Tudor Avram & Levan Tsinadze

09:00-12:30 January 25

AMLD / Global partners