“Counter-Strike Deathmatch With Large-Scale Behavioral Cloning”, Tim Pearce, Jun Zhu2021-04-09 (imitation learning, model-free RL; similar)⁠:

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 human-like 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 behavioral 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.

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