DECOY

🎯 A high-fidelity CS:GO simulation environment for strategic multi-agent planning research. DECOY transforms complex 3D tactical gameplay into efficient discretized simulations while preserving environmental realism. Using neural models trained on real tournament data, it enables researchers to study strategic decision-making without the computational overhead of low-level game mechanics. Perfect for advancing multi-agent AI research in competitive scenarios.

Quick Start

# Setup environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

# Explore the simulation
python inspector_demo.py

Features

• Discretized Strategic Planning: High-level tactical decisions without low-level mechanics
• Real Data Integration: Neural models trained on professional CS:GO tournament data
• Efficient Simulation: Computationally lightweight while maintaining environmental fidelity
• Research Ready: Built for multi-agent planning and behavior generation research

Roadmap

• MARL training examples
• Environment customization tools
• Interactive waypoint visualizer

Citation

@inproceedings{wang2025csgo,
  author    = {Yunzhe Wang and Volkan Ustun and Chris McGroarty},
  title     = {A data-driven discretized {CS:GO} simulation environment to facilitate strategic multi-agent planning research},
  booktitle = {Proceedings of the 2025 Winter Simulation Conference (WSC)},
  year      = {2025},
  address   = {Los Angeles, CA, USA},
  publisher = {IEEE},
}