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.

本文关注 AWS DeepRacer 的物理环境能力。通过两个实验，研究了超参数和物体避让对物理环境的影响，发现在模拟环境中，较高的梯度下降批次大小和 Huber 损失类型的模型表现较好，在物理环境中，梯度下降批次大小为 128 时效果最佳。同时发现物体避让在模拟环境中有效，但在物理环境中仍面临挑战。