# On games and simulators as a platform for development of artificial intelligence for command and control

arXiv: https://arxiv.org/abs/2110.11305 · HTML: https://mlsanity.com/paper/2110.11305/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 76 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [StarCraft II: A New Challenge for Reinforcement Learning](https://mlsanity.com/paper/1708.04782/) (2017)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II](https://mlsanity.com/paper/2012.13169/) (2020)
- [TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game](https://mlsanity.com/paper/2011.13729/) (2020)
- [A Narration-based Reward Shaping Approach using Grounded Natural Language Commands](https://mlsanity.com/paper/1911.00497/) (2019)
- [TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game](https://mlsanity.com/paper/1809.07193/) (2018)
- [Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time](https://mlsanity.com/paper/1810.11545/) (2018)
- [Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning](https://mlsanity.com/paper/1906.02671/) (2019)
- [Coordination-driven learning in multi-agent problem spaces](https://mlsanity.com/paper/1809.04918/) (2018)
- [Adapting the Predator-Prey Game Theoretic Environment to Army Tactical Edge Scenarios with Computational Multiagent Systems](https://mlsanity.com/paper/1807.05806/) (2018)

## Built on by (most cited first, 3 of 3)

- [COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations](https://mlsanity.com/paper/2402.01786/) (2024)
- [Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II](https://mlsanity.com/paper/2205.05784/) (2022)
- [Word Play for Playing Othello (Reverses)](https://mlsanity.com/paper/2207.08766/) (2022)

## Abstract (arXiv)

Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces. These characteristics have attracted the artificial intelligence (AI) community by supporting development of algorithms with complex benchmarks and the capability to rapidly iterate over new ideas. The success of artificial intelligence algorithms in real-time strategy games such as StarCraft II have also attracted the attention of the military research community aiming to explore similar techniques in military counterpart scenarios. Aiming to bridge the connection between games and military applications, this work discusses past and current efforts on how games and simulators, together with the artificial intelligence algorithms, have been adapted to simulate certain aspects of military missions and how they might impact the future battlefield. This paper also investigates how advances in virtual reality and visual augmentation systems open new possibilities in human interfaces with gaming platforms and their military parallels.