D2RL: Deep Dense Architectures in Reinforcement Learning

In this article I want to give a quick presentation of the D2RL paper, applying deep dense architectures of neural networks for Deep Reinforcement Learning. The effect of large and dense network architectures has been long explored in Computer Vision and Deep Learning. The improved performance and other benefits of such dense models compared to shallow ones are widely established. Thereby, in Deep Reinforcement Learning, neural network architectures haven't gotten that much attention yet. Commonly used networks like policy or Q-function are usually only two layers deep.