TL:DR: We have updated the starter kit: it is now a full RL baseline that you can easily train and submit out of the box! https://gitlab.aicrowd.com/flatland/neurips2020-flatland-starter-kit
Making your first submission can be challenging: you need to get familiar with the Flatland APIs, discover the submission process, write out all your dependencies in the apt.txt and environment.yml files…
This can take a few tries to get right, and is not particularly satisfying!
To help with this process, we have updated the starter kit. It is now a full PyTorch-powered DQN baseline that you can submit right away.
Trainable for free on Colab
You can train a full agent using the starter kit running everything on Colab. Colab is a free notebook service that allows you to run code in the cloud for free. You can even use GPUs if you want to experiment with larger networks!
Easy to tweak and extend
The training script exposes many parameters to quickly test hypotheses:
- Epsilon decay (start, end, and decay rate)
- Buffer size and min size before training starts
- Learning rate, gamma, tau…
See here for the full list of command line parameters: https://gitlab.aicrowd.com/flatland/neurips2020-flatland-starter-kit#sample-training-usage
Easy hyper-parameter tuning
You can use the (free) Weight & Biases service to log experiment results and to automate hyperparameter sweeps:
Sample report: https://wandb.ai/masterscrat/flatland-examples-reinforcement_learning/reports/Flatland-Examples--VmlldzoxNDI2MTA
Documentation: https://docs.wandb.com/sweeps
Provided checkpoint
The starter kit comes with a sample checkpoint which should allow you to reach ~50 points on the leaderboard straight away. As of right now, this would put you in the top 15!
Old starter kit
The previous version is available in the old-starter-kit branch as a reference: