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[N] Flatland Challenge – Multi-Agent Reinforcement Learning for Transportation Systems

Hi all

We launched the Flatland Challenge, which is an official challenge of the Applied Machine Learning Days.

Flatland: Multi-Agent Reinforcement Learning Challenge

The Flatland Challenge is a competition to foster progress in multi-agent reinforcement learning for real world applications. The re-scheduling problem (RSP), which has traditionally been approached by operations research, serves as an excellent challenge to investigate the possibilies of deep learning for planning in stochastic environments. Different rounds with increasing difficulty and the presence of stochasticity in the environment encourage participants to look beyond classical planning algorithms and come up with solutions for the transport management systems of the future.

The Challenge

The challenge requires your creativity and savviness. In 2 submission rounds with increasing difficulty, you can prove that you have what it takes. We invite you to enter the race with your unique solution and to win great prizes – at the same time solving one of the key challenges in the world of transportation!

In contrast to most reinforcement learning challenges the focus of this challenge is not solely on the submission of great algorithms as controllers. We encourage the participants to come up with novel observation spaces for this challenge and share them with the community (community prize awarded) to improve performance on this task.

Real world applications

The Swiss Federal Railways (SBB) operate the densest mixed railway traffic in the world. SBB maintain and operate the biggest railway infrastructure in Switzerland. Today, there are more than 10,000 trains running each day, being routed over 13,000 switches and controlled by more than 32,000 signals. Each day 1.2 million passengers and almost half of Switzerland’s volume of transported goods are transported on this railway network. Due to the growing demand for mobility, SBB needs to increase the transportation capacity of the network by approximately 30% in the future.

The increase in transport capacity can be achieved through different measures, such as denser train schedules, investments in new infrastructure, and/or investments in new rolling stock. However, SBB currently lack suitable technologies and tools to quantitatively assess these different measures.

The SBB are therefore looking for novel approaches that can help revolutionize the transportation system of the future.

Prizes

Your problem solutions mean something to us – hence prizes with a total value of 30k CHF (approx. 30k USD) are reserved for those with the best submissions. You can excel in two categories: The best solution category and the community prize category. Within both those categories your submission is individually ranked taking into account your performance in Round 1 and Round 2. Make sure to check the participation rules before you start. Only submissions conforming to our rules have a chance of winning the prizes.

Best Solution Prize: Won by the participants with the best performing submission on our test set. Both of your rankings from the Round 1 and Round 2 are taken into account. Check the leader board on this site regularly for the latest information on your ranking.

The top three submissions in this category will be awarded the following cash prizes (in Swiss Francs):

CHF 7’500.- (~USD 7’500) for first prize

CHF 5’000.- (~USD 5’000) for second prize

CHF 2’500.- (~USD 2’500) for third prize

Community Contributions Prize: Awarded to the person/group who makes the biggest contribution to the community – done through generating new observations and sharing them with the community.

The top submission in this category will be awarded the following cash prize (in Swiss Francs): CHF 5’000.- (~USD 5’000)

In addition, we will hand-pick and award up to five (5) travel grants to the Applied Machine Learning Days 2019 in Lausanne, Switzerland. Participants with promising solutions may be invited to present their solutions at SBB in Bern, Switzerland.

Note: It is possible for a participant to win in both categories

Participate

Are you up for the challenge? More information about the Flatland Challenge can be found here.

Contribute

Want to help improve and build upon Flatland?

Head over to our gitlab repo to see how you can contribute shaping this environment.

Contact

For Challenge-related questions (technical and/or content questions):

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

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

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

submitted by /u/ML_Erik
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.