  In autonomous embedded systems, it is often vital to reduce the amount of
actions taken in the real world and energy required to learn a policy. Training
reinforcement learning agents from high dimensional image representations can
be very expensive and time consuming. Autoencoders are deep neural network used
to compress high dimensional data such as pixelated images into small latent
representations. This compression model is vital to efficiently learn policies,
especially when learning on embedded systems. We have implemented this model on
the NVIDIA Jetson TX2 embedded GPU, and evaluated the power consumption,
throughput, and energy consumption of the autoencoders for various CPU/GPU core
combinations, frequencies, and model parameters. Additionally, we have shown
the reconstructions generated by the autoencoder to analyze the quality of the
generated compressed representation and also the performance of the
reinforcement learning agent. Finally, we have presented an assessment of the
viability of training these models on embedded systems and their usefulness in
developing autonomous policies. Using autoencoders, we were able to achieve 4-5
$\times$ improved performance compared to a baseline RL agent with a
convolutional feature extractor, while using less than 2W of power.
