## Examples of scripts to train agents in the Flatland environment.


# Torch Training
The `torch_training` folder shows an example of how to train agents with a DQN implemented in pytorch.
In the links below you find introductions to training an agent on Flatland:

- Training an agent for navigation ([Introduction](https://gitlab.aicrowd.com/flatland/baselines/blob/master/torch_training/Getting_Started_Training.md))
- Training multiple agents to avoid conflicts ([Introduction](https://gitlab.aicrowd.com/flatland/baselines/blob/master/torch_training/Multi_Agent_Training_Intro.md)) 

Use this introductions to get used to the Flatland environment. Then build your own predictors, observations and agents to improve the performance even more and solve the most complex environments of the challenge.
# RLLib Training
The `RLLib_training` folder shows an example of how to train agents with  algorithm from implemented in the RLLib library available at: <https://github.com/ray-project/ray/tree/master/python/ray/rllib>