  This paper describes an AI agent that plays the popular first-person-shooter
(FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input.
The agent, a deep neural network, matches the performance of the medium
difficulty built-in AI on the deathmatch game mode, whilst adopting a humanlike
play style. Unlike much prior work in games, no API is available for CSGO, so
algorithms must train and run in real-time. This limits the quantity of
on-policy data that can be generated, precluding many reinforcement learning
algorithms. Our solution uses behavioural cloning - training on a large noisy
dataset scraped from human play on online servers (4 million frames, comparable
in size to ImageNet), and a smaller dataset of high-quality expert
demonstrations. This scale is an order of magnitude larger than prior work on
imitation learning in FPS games.
