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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