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Warm-Up Round: Completed Round 1: Completed Round 2: 10 days left #neurips #reinforcement_learning

🚉 Introduction

🏆 Community Prize: 1,000 CHF Prize Pool! Join now

🏃 Want to dive straight in? Make your first submission in 10 minutes

Train a complete solution directly on Colab! DQN / PPO

🏁 Round 2 has started! Read what's new from Round 1

chat on Discord

This challenge tackles a key problem in the transportation world: 
How to efficiently manage dense traffic on complex railway networks?

This is a real-world problem faced by many transportation and logistics companies around the world such as the Swiss Federal Railways and Deutsche Bahn. Your contribution may shape the way modern traffic management systems are implemented, not only in railway but also in other areas of transportation and logistics!

🚂 Background

The Flatland challenge aims to address the problem of train scheduling and rescheduling by providing a simple grid world environment and allowing for diverse experimental approaches.

This is the second edition of this challenge. In the first one, participants mainly used solutions from the operations research field. In this second edition we are encouraging participants to use solutions which leverage the recent progress in reinforcement learning.

🔗 The Flatland environment

🔗 Past winning solutions

Flatland preview

Flatland: the core task of this challenge is to manage and maintain railway traffic on complex scenarios in complex networks

📜 Tasks

Your goal is to make all the trains arrive at their target destination with minimal travel time. In other words, we want to minimize the number of steps that it takes for each agent to reach its destination. In the simpler levels, the agents may achieve their goals using ad-hoc decisions. But as the difficulty increases, the agents have to be able to plan ahead!

Teaser

Problem example: this is a teaser of what we expect you to do

A central question while designing an agent is the observations used to take decisions. As a participant, you can either work with one of the base observations that are provided or better, design an improved observation yourself!

These are the three provided observations:

  • Global Observation: The whole scene is observed.
  • Local Grid Observation: A local grid around the agent is observed.
  • Tree Observation: The agent can observe its navigable path to some predefined depth.

🔗 Observations in Flatland

🔗 Create custom observations

⚖ Evaluation metrics

The primary metric is the normalized return from your agents - the higher the better.

For each episode, the minimum possible value is -1.0, which occurs if none of the agents reach their goal. The maximum possible value is 0.0, which would occur if all the agents reached their targets in one time step, which is generally not achievable.

The agents have to solve as many episodes as possible. During each evaluation, the agents have to solve environments of increasingly large size. The evaluation stops when the agents don't perform well enough anymore, or after 8 hours, whichever comes first. Read the documentation for more details:

🔗 Evaluation metrics

🔗 Evaluation environments

🏆 Prizes

The prizes are four travel grants to come visit us at EPFL (Switzerland), a GeForce RTX 2080 Graphics Card and 3 NVIDIA Jetson Nano Developer Kit.

  • The first place team in the final round will be awarded one travel grant, whichever approach they use.
  • The top three teams in the final round which use a reinforcement learning approach for their winning submission will be awarded one travel grant each.
  • The first place team in the final round which uses a reinforcement learning approach for their winning submission will be awarded the RTX 2080 GPU.
  • The second place team which uses a reinforcement learning approach, as well as the top two teams which use another approach for their winning submission will be awarded a Jetson Nano Kit.

The approach used for each submission needs to be specified in the aicrowd.json file as described in the submission guide.

The winning submissions will be verified manually by the organizers to ensure the method used matches what has been declared in the aicrowd.json file. The organizers have the final word when judging the validity of each submission.

If the overall first place team uses a reinforcement learning approach, then this team will be awarded two travel grants.

📝 Community Prizes

To encourage collaboration and novel problem-solving, an additional 500 CHF prize pool will be awarded to participants that create any kind of material that helps the community better understand the Flatland environment before 22nd October.

🔗 Details and conditions

🔗 Contributions so far

📅 Timeline

Here's the tentative timeline:

  • June 1st - July 9th: Warm-Up Round
  • July 10th - September 6th: Round 1
  • September 6th - November 6th: Round 2
  • October 23rd: Team Freeze
  • November 6th - November 15th: Post Challenge Analysis
  • November 15th: Final Results Announced
  • October 6th - November 15th: Post Challenge Wrap-Up

There are no qualifying rounds: participants can join the challenge at any point until the final deadline. Prizes will be awarded according to Round 2 ranking.

🚉 Next stops

The Flatland documentation contains everything you need to get started with this challenge!

Want to dive straight in? 
🔗 Submit in 10 minutes

New to multi-agent reinforcement learning? 
🔗 Step by step guide

Want to explore advanced solutions such as distributed training and imitation learning?
🔗 Research baselines

📱 Contact

Join the Discord channel to exchange with other participants!

🔗 Discord Channel

If you have a problem or question for the organizers, use either the Discussion Forum or open an issue:

🔗 Discussion Forum

🔗 Technical Issues

We strongly encourage you to use the public channels mentioned above for communications between the participants and the organizers. But if you're looking for a direct communication channel, feel free to reach out to us at:

  • mohanty [at] aicrowd.com
  • florian [at] aicrowd.com
  • erik.nygren [at] sbb.ch

For press inquiries, please contact SBB Media Relations at press@sbb.ch

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