Flatland Community Prize 
Contribution Ideas
Best solutions to a problem like Flatland are found in collaborations and learnings from multiple approaches. To facilitate sharing of ideas and discussions, we invite you to share tools & tips, resources & approaches you follow with the Flatland community. 
Flatland advances state-of-the-art work in multi-agent reinforcement learning, and your contributions can play a key part in that advancement!
If you are confused about what kind of Contributions you can make, we are listing down some ideas in this doc that you can follow. 
We hope they inspire you to share your ideas and insights with the Flatland community!
Simple explainer videos
- Flatland is a complex problem that may be hard to get into. We have worked hard to revamp its documentation, create new starter kits and baseline examples, but it is still challenging to make your first steps with this environment!
- We would welcome simple videos that give a high-level overview of this problem, and that walk newcomers through their first experiments with it.
Investigate the use of new methods
- We have released a number of baselines that show results using methods such as PPO, Ape-X, imitation learning algorithms etc.
- There are still many interesting methods that could be explored! It would be interesting to investigate the performance to see which one are the most promising:
- PPG is a brand new improvement of PPO that shows improved sample efficiency.
- PLR is another bleeding edge method specifically for procedurally-generated environments. The idea is to find and prioritize the environment seeds that provide the most learning potential.
- MADDPG is an extension of DDPG for MARL environments. Most of the methods we have investigated so far are designed for single-agent environments, and we simply consider the other agents as being part of the environment. But this is not optimal! MADDPG handles this properly.
Improve the rendering of the environment
- Looking at trains evolving on a large grid according to your policy is quite satisfying!
- But maybe you can think of ways to make this look even better.
- For example, maybe by placing the trees in more realistic ways? Or by scaling up some of the graphics?
- This rendering uses the same asset, but with nicer placement and aesthetics
- If you look in the flatland repository, you can actually find a lot of very nice assets waiting to be used!
- https://gitlab.aicrowd.com/flatland/flatland/tree/master/flatland/png
