  We present the first prize solution to NeurIPS 2021 - AWS Deepracer
Challenge. In this competition, the task was to train a reinforcement learning
agent (i.e. an autonomous car), that learns to drive by interacting with its
environment, a simulated track, by taking an action in a given state to
maximize the expected reward. This model was then tested on a real-world track
with a miniature AWS Deepracer car. Our goal is to train a model that can
complete a lap as fast as possible without going off the track. The Deepracer
challenge is a part of a series of embodied intelligence competitions in the
field of autonomous vehicles, called The AI Driving Olympics (AI-DO). The
overall objective of the AI-DO is to provide accessible mechanisms for
benchmarking progress in autonomy applied to the task of autonomous driving.
The tricky section of this challenge was the sim2real transfer of the learned
skills. To reduce the domain gap in the observation space we did a canny edge
detection in addition to cropping out of the unnecessary background
information. We modeled the problem as a behavioral cloning task and used
MLP-MIXER to optimize for runtime. We made sure our model was capable of
handling control noise by careful filtration of the training data and that gave
us a robust model capable of completing the track even when 50% of the commands
were randomly changed. The overall runtime of the model was only 2-3ms on a
modern CPU.
