diff --git a/README.md b/README.md index d8dc09bde2ace60244a901506eb25d1790acb74a..8e72c905d68535ab0073e15ade1be95b128add71 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,12 @@ -## Examples of scripts to train agents in the Flatland environment. +# âš ï¸ Deprecated repository -# Torch Training +This repository is deprecated! Please go to: + +#### **https://gitlab.aicrowd.com/flatland/flatland-examples** + + +## 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: @@ -15,7 +20,7 @@ With the above introductions you will solve tasks like these and even more...  -# Sequential Agent +## Sequential Agent This is a very simple baseline to show you have the `complex_level_generator` generates feasible network configurations. If you run the `run_test.py` file you will see a simple agent that solves the level by sequentially running each agent along its shortest path. This is very innefficient but it solves all the instances generated by `complex_level_generator`. However when being scored for the AIcrowd competition, this agent fails due to the duration it needs to solve an episode.