diff --git a/README.md b/README.md index b1622a0f7f06c86fe424b9380fa127b2b0315d3b..cdd54b5229cd88c94618782bf242595b453413f8 100644 --- a/README.md +++ b/README.md @@ -18,3 +18,11 @@ With the above introductions you will solve tasks like these and even more... # 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> +# 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. + +Here you see it in action: + + \ No newline at end of file