In the AI for TSP competition we try to solve optimization problems using machine learning.

Related tags

Deep Learningreinforcement-learningsurrogate-based-optimization
Overview

AI for TSP Competition

Goal

In the AI for TSP competition we try to solve optimization problems using machine learning. The competition will be hosted at the Data Science meets Optimization workshop at IJCAI21 and consists of two tracks:

  • Online supervised learning using surrogate models
  • Reinforcement learning

The goal of this competition is to strengthen the relation between the machine learning field and the optimization field. You can learn more about the competition here.

Prizes

Cash prizes will be announced soon!

Timeline

  • May 7: Start of the tryout period
  • May 21: Competition start
  • July 5: Submission deadline (validation)
  • July 12: Submission deadline (test)
  • August 9: Winners are contacted privately
  • August 21/22: Public announcement of winners

Whitepaper

For more details about the competition, please refer to this document.

Official Documentation

Check out our Documentation

Announcements

Check out our Announcements

FAQ

Check out our FAQ

Slack Channel

Check out our Slack Channel

Dependencies

  • Python=3.8 (should be OK with v >= 3.6)
  • PyTorch=1.8 (track 2 only)
  • Numpy=1.20
  • bayesian-optimization=1.1.0 (track 1 only)
  • Pandas=1.2.4
  • Conda=4.8.4 (optional)

Please check environment.yml

Acknowledgments

Special thanks to https://github.com/pemami4911/neural-combinatorial-rl-pytorch for the implemetation of Neural CO used as part of this repository.

Owner
Paulo da Costa
Paulo da Costa
GitHub Repository https://www.tspcompetition.com/
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