⚠️ A valid COVID certificate must be presented on site to enter the event. ⚠️

This workshop is a great introduction to the Enabling cooperation between multiple agents: information sharing in Multi-Agent Reinforcement Learning workshop.

Scheduling trains is hard: railway networks are growing fast, and the decision-making methods commonly used don't scale well. How can we solve this problem?

With machine learning, of course! In this workshop, we will use reinforcement learning to tackle this real-world challenge.

In the morning, we will introduce the main reinforcement learning methods. Participants will get familiar with them by solving toy problems. In the afternoon, participants will design their own agents, which will then compete with other people’s agents in a (friendly) competitive setting.

We will use the Flatland railway simulator, developed in collaboration with SBB and Deutsche Bahn. We plan to invite SBB researchers to give insights on this problem, as well as competitive participants from previous Flatland challenges.

Following this workshop, participants can take part in the other full-day Flatland workshop organized by Deutsche Bahn and InstaDeep, which will introduce the bleeding-edge innovations they have been working on to tackle this problem.

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 multi-agent competitive setting.

• Each participant is expected to actively take part by designing and training RL agents on their machine. 
• Participants can also form teams, working on the same laptop (assuming end of global pandemic).
• The training will be done on the Google Colab service, which is free but requires a Google account.
• 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."