Counter-Strike Deathmatch with Large-Scale Behavioural Cloning

• URL: http://arxiv.org/abs/2104.04258v1
• Date: Fri, 9 Apr 2021 09:12:12 GMT
• Title: Counter-Strike Deathmatch with Large-Scale Behavioural Cloning
• Authors: Tim Pearce, Jun Zhu
• Abstract summary: This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game Counter-Strike; Global Offensive' 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.
• Score: 34.22811814104069
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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