A Data-Driven Discretized CS:GO Simulation Environment to Facilitate Strategic Multi-Agent Planning Research

Using Counter-Strike: Global Offensive (CS:GO) as a testbed, our framework accurately simulates gameplay using only movement decisions as tactical positioning--without explicitly modeling low-level mechanics such as aiming and shooting. Central to our approach is a waypoint system that simplifies and discretizes continuous states and actions, paired with neural predictive and generative models trained on real CS:GO tournament data to reconstruct event outcomes. Extensive evaluations show that replays generated from human data in DECOY closely match those observed in the original game. Our publicly available simulation environment provides a valuable tool for advancing research in strategic multi-agent planning and behavior generation. 1 INTRODUCTION Team-based multiplayer strategy games have emerged as grand challenge domains for multi-agent learning and long-horizon planning. Breakthroughs in complex games such as StarCraft II and Dota 2--where AI agents have achieved human-expert or even superhuman performance through large-scale self-play training (Baker et al. 2019; Vinyals et al. 2019; Berner et al. 2019; Open Ended Learning Team et al. 2021)--demonstrate that, given sufficient simulation and training, sophisticated strategies can be discovered even in environments characterized by long time horizons, imperfect information, and high-dimensional state-action spaces. However, these successes come at the expense of enormous computational costs, and a prevailing trend in the field is to scale performance by increasing model size and training on larger datasets with more simulation steps (Neumann and Gros 2022; Obando-Ceron et al. 2024; Kaplan et al. 2020).