This README provides an overview of how I approached the AWS DeepRacer Student League Competition in the last 3 years. Over the course of the model's development it was necessary to define an action space, develop a reward function for reinforcement learning, and experiment with various hyperparameters controlling the underlying 3-layer neural network.
The Pre-Season track is the 2021 DeepRacer Championship Cup track named Vivalas Loop, which is a relatively straightforward loop with minor turns.
Initially, I trained the model with much simpler parameters on the Vivalas track using a centreline-following reward function with an incentive for faster speeds while travelling straight.
The straight forward function for the above can be seen in this code snippet from reward_simple.py:
reward_funtion_L65.py has the code for actual function I used for which the docs will be added as soon as possible.
- AWS DeepRacer League (https://aws.amazon.com/deepracer/league/)
- DeepRacer Logs Analysis tools (https://github.com/aws-deepracer-community/deepracer-analysis)
- AWS Deepracer Workshop Lab Github (https://github.com/aws-samples/aws-deepracer-workshops)
- DeepRacer K1999 Racing Line Algo (https://github.com/cdthompson/deepracer-k1999-race-lines)
- DeepRacer Utils CLI commands (https://github.com/aws-deepracer-community/deepracer-utils)
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