mlsanityData to 13 Oct 2026

Learning Environment for the Air Domain (LEAD)

arXiv 2304.14423 ↗First posted 27 Apr 2023cs.LGcited by 1

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Abstract (arXiv)

A substantial part of fighter pilot training is simulation-based and involves computer-generated forces controlled by predefined behavior models. The behavior models are typically manually created by eliciting knowledge from experienced pilots, which is a time-consuming process. Despite the work put in, the behavior models are often unsatisfactory due to their predictable nature and lack of adaptivity, forcing instructors to spend time manually monitoring and controlling them. Reinforcement and imitation learning pose as alternatives to handcrafted models. This paper presents the Learning Environment for the Air Domain (LEAD), a system for creating and integrating intelligent air combat behavior in military simulations. By incorporating the popular programming library and interface Gymnasium, LEAD allows users to apply readily available machine learning algorithms. Additionally, LEAD can communicate with third-party simulation software through distributed simulation protocols, which allows behavior models to be learned and employed using simulation systems of different fidelities.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 27 Apr 2023.

Builds on

6 of 28 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 14,592
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,472
  3. 2016OpenAI Gymcited by 1,694
  4. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,093
  5. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 259
  6. 2021On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learningcited by 59

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024A survey of air combat behavior modeling using machine learningcited by 3

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  1. 2021Adaptive Synthetic Characters for Military Training
  2. 2023An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data
  3. 2023Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback
  4. 2019Learning a Behavioral Repertoire from Demonstrations
  5. 2023Pilot Performance modeling via observer-based inverse reinforcement learning
  6. 2026COMBAT: Conditional World Models for Behavioral Agent Training
  7. 2024Automatic Control With Human-Like Reasoning: Exploring Language Model Embodied Air Traffic Agents
  8. 2022Augmenting Flight Training with AI to Efficiently Train Pilots