@@ -18,3 +18,11 @@ With the above introductions you will solve tasks like these and even more...
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@@ -18,3 +18,11 @@ With the above introductions you will solve tasks like these and even more...
# RLLib Training
# 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>
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.
