  Deep Reinforcement Learning (DRL) has produced great achievements since it
was proposed, including the possibility of processing raw vision input data.
However, training an agent to perform tasks based on image feedback remains a
challenge. It requires the processing of large amounts of data from
high-dimensional observation spaces, frame by frame, and the agent's actions
are computed according to deep neural network policies, end-to-end. Image
pre-processing is an effective way of reducing these high dimensional spaces,
eliminating unnecessary information present in the scene, supporting the
extraction of features and their representations in the agent's neural network.
Modern video-games are examples of this type of challenge for DRL algorithms
because of their visual complexity. In this paper, we propose a low-dimensional
observation filter that allows a deep Q-network agent to successfully play in a
visually complex and modern video-game, called Neon Drive.
