  Vision Transformers (ViT) have recently demonstrated the significant
potential of transformer architectures for computer vision. To what extent can
image-based deep reinforcement learning also benefit from ViT architectures, as
compared to standard convolutional neural network (CNN) architectures? To
answer this question, we evaluate ViT training methods for image-based
reinforcement learning (RL) control tasks and compare these results to a
leading convolutional-network architecture method, RAD. For training the ViT
encoder, we consider several recently-proposed self-supervised losses that are
treated as auxiliary tasks, as well as a baseline with no additional loss
terms. We find that the CNN architectures trained using RAD still generally
provide superior performance. For the ViT methods, all three types of auxiliary
tasks that we consider provide a benefit over plain ViT training. Furthermore,
ViT reconstruction-based tasks are found to significantly outperform ViT
contrastive-learning.
