This repository contains the code that was used for the article "An Advanced Guide to AWS DeepRacer - Autonomous Formula 1 Racing using Reinforcement Learning". Feel free to check it out here.
- The folder Compute_Speed_And_Actions contains a jupyter notebook, which takes the optimal racing line from this repo and computes the optimal speed. Additionally, it computes a custom action space with K-Means clustering. The folder also contains the K1999 racing line notebook from cdthompson, which I altered to be able to only use the inner 80% of the track.
- The folder Reward_Function contains a .py file with the reward function that our team used to get to 12th place out of 1291 participants in the time trial category of the F1 event in May 2020
- The folder Selenium_Automation contains a jupyter notebook, which allows you to submit a model to a race multiple times without using the AWS CLI. As a bonus, you can also automatically conducts experiments with hyperparameters. This can be used to conduct multiple experiments over night without having to manually set them up every couple of hours
- To calculate the optimal racing line: https://github.com/cdthompson/deepracer-k1999-race-lines
- To analyze the logs: https://github.com/aws-deepracer-community/deepracer-analysis
- To retreive the track data: https://github.com/aws-deepracer-community/deepracer-simapp/tree/master/bundle/deepracer_simulation_environment/share/deepracer_simulation_environment/routes
Feel free to use, distribute, and alter the code as you like.
This is a finished university project. Therefore, we will not be maintaining the code any more.
