# Counter-Strike Deathmatch with Large-Scale Behavioural Cloning

arXiv: https://arxiv.org/abs/2104.04258 · HTML: https://mlsanity.com/paper/2104.04258/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 15 of 15)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Causal Confusion in Imitation Learning](https://mlsanity.com/paper/1905.11979/) (2019)
- [Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://mlsanity.com/paper/1807.01281/) (2018)
- [MineRL: A Large-Scale Dataset of Minecraft Demonstrations](https://mlsanity.com/paper/1907.13440/) (2019)
- [ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning](https://mlsanity.com/paper/1605.02097/) (2016)
- [Playing FPS Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05521/) (2016)
- [DeepMind Lab](https://mlsanity.com/paper/1612.03801/) (2016)
- [ViZDoom Competitions: Playing Doom from Pixels](https://mlsanity.com/paper/1809.03470/) (2018)
- [SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II](https://mlsanity.com/paper/2012.13169/) (2020)
- [Imitation Learning with Concurrent Actions in 3D Games](https://mlsanity.com/paper/1803.05402/) (2018)
- [Benchmarking End-to-End Behavioural Cloning on Video Games](https://mlsanity.com/paper/2004.00981/) (2020)
- [The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI](https://mlsanity.com/paper/1702.05663/) (2017)

## Built on by (most cited first, 30 of 50)

- [Diffusion for World Modeling: Visual Details Matter in Atari](https://mlsanity.com/paper/2405.12399/) (2024)
- [Imitating Human Behaviour with Diffusion Models](https://mlsanity.com/paper/2301.10677/) (2023)
- [Vid2World: Crafting Video Diffusion Models to Interactive World Models](https://mlsanity.com/paper/2505.14357/) (2025)
- [Scaling Instructable Agents Across Many Simulated Worlds](https://mlsanity.com/paper/2404.10179/) (2024)
- [SIMA 2: A Generalist Embodied Agent for Virtual Worlds](https://mlsanity.com/paper/2512.04797/) (2025)
- [TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play](https://mlsanity.com/paper/2302.07515/) (2023)
- [StateSpaceDiffuser: Bringing Long Context to Diffusion World Models](https://mlsanity.com/paper/2505.22246/) (2025)
- [Model-based Trajectory Stitching for Improved Offline Reinforcement Learning](https://mlsanity.com/paper/2211.11603/) (2022)
- [Rethinking Latent Redundancy in Behavior Cloning: An Information Bottleneck Approach for Robot Manipulation](https://mlsanity.com/paper/2502.02853/) (2025)
- [Model-based trajectory stitching for improved behavioural cloning and its applications](https://mlsanity.com/paper/2212.04280/) (2022)
- [Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning](https://mlsanity.com/paper/2310.12609/) (2023)
- [ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment](https://mlsanity.com/paper/2503.02505/) (2025)
- [Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games](https://mlsanity.com/paper/2312.02312/) (2023)
- [Predicting Long-Term Human Behaviors in Discrete Representations via Physics-Guided Diffusion](https://mlsanity.com/paper/2405.19528/) (2024)
- [BehAVE: Behaviour Alignment of Video Game Encodings](https://mlsanity.com/paper/2402.01335/) (2024)
- [Training Interactive Agent in Large FPS Game Map with Rule-enhanced Reinforcement Learning](https://mlsanity.com/paper/2410.04936/) (2024)
- [Toward Stable World Models: Measuring and Addressing World Instability in Generative Environments](https://mlsanity.com/paper/2503.08122/) (2025)
- [Adapting a World Model for Trajectory Following in a 3D Game](https://mlsanity.com/paper/2504.12299/) (2025)
- [Nano World Models: A Minimalist Implementation of Future Video Prediction](https://mlsanity.com/paper/2605.23993/) (2026)
- [Self-Imitation Learning from Demonstrations](https://mlsanity.com/paper/2203.10905/) (2022)
- [Improving Generalization in Game Agents with Data Augmentation in Imitation Learning](https://mlsanity.com/paper/2309.12815/) (2023)
- [Learning to Move Like Professional Counter-Strike Players](https://mlsanity.com/paper/2408.13934/) (2024)
- [Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space](https://mlsanity.com/paper/2410.01796/) (2024)
- [D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI](https://mlsanity.com/paper/2510.05684/) (2025)
- [Reinforcement Learning for High-Level Strategic Control in Tower Defense Games](https://mlsanity.com/paper/2406.07980/) (2024)
- [Real-Time Diffusion Policies for Games: Enhancing Consistency Policies with Q-Ensembles](https://mlsanity.com/paper/2503.16978/) (2025)
- [Pixels to Play: A Foundation Model for 3D Gameplay](https://mlsanity.com/paper/2508.14295/) (2025)
- [TV2TV: A Unified Framework for Interleaved Language and Video Generation](https://mlsanity.com/paper/2512.05103/) (2025)
- [When Does Predictive Inverse Dynamics Outperform Behavior Cloning?](https://mlsanity.com/paper/2601.21718/) (2026)
- [Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse](https://mlsanity.com/paper/2605.09965/) (2026)

## Abstract (arXiv)

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.