Deep Reinforcement Learning Using a Low-Dimensional Observation Filter for Visual Complex Video Game Playing
This work addresses the problem of high-dimensional observation spaces in DRL for video game playing, but it is incremental as it builds on existing pre-processing methods.
The authors tackled the challenge of training deep reinforcement learning agents on high-dimensional visual inputs in complex video games by proposing a low-dimensional observation filter, which enabled a deep Q-network agent to successfully play the visually complex game Neon Drive.
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.