  This paper focuses on the physical racetrack capabilities of AWS DeepRacer.
Two separate experiments were conducted. The first experiment (Experiment I)
focused on evaluating the impact of hyperparameters on the physical
environment. Hyperparameters such as gradient descent batch size and loss type
were changed systematically as well as training time settings. The second
experiment (Experiment II) focused on exploring AWS DeepRacer object avoidance
in the physical environment. It was uncovered that in the simulated
environment, models with a higher gradient descent batch size had better
performance than models with a lower gradient descent batch size.
Alternatively, in the physical environment, a gradient descent batch size of
128 appears to be preferable. It was found that models using the loss type of
Huber outperformed models that used the loss type of MSE in both the simulated
and physical environments. Finally, object avoidance in the simulated
environment appeared to be effective; however, when bringing these models to
the physical environment, there was a pronounced challenge to avoid objects.
Therefore, object avoidance in the physical environment remains an open
challenge.
