One of our Fortune 500 clients in the automobile industry wanted to analyze and improve their racetrack experience. The racing car is equipped with more than 100 sensors and these were programmed to capture all activities of the car such as steering wheel angle, acceleration, engine running state, etc.
The GoodWorkLabs AI team was given the task to identify the optimal path of the vehicle by analyzing the complete racing track with other tracks and also the overall racing experience.
We first analyzed the track using sensor data and then implemented state-of-the-art Deep Q-learning with Tensorflow.
Our Deep Q Neural Network took a stack of n frames as input. These pass through the network, and output a vector of Q-values for each action possible in the given state. We then take the biggest Q-value of this vector to find our best action.
In the beginning, the agent does not perform well. But with time and continuous learning, it began to associate frames (states) with best actions. Pre-processing was a very important step as we wanted to reduce the complexity of our states and reduce the computation time needed for training.
For that to happen, we greyscale each of our states. Color does not add important information (in our case, we just had to find the optimal path). This is an important saving since we reduced our three color channels (RGB) to 1 (greyscale).
The tech stack used to develop this model was Python, PyTorch.
Below are some visualizations of the optimal path identified by our AI model.
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