mlsanityData to 14 Oct 2026

Training Environment for High Performance Reinforcement Learning

arXiv 2505.01953 ↗First posted 4 May 2025cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This paper presents Tunnel, a simple, open source, reinforcement learning training environment for high performance aircraft. It integrates the F16 3D nonlinear flight dynamics into OpenAI Gymnasium python package. The template includes primitives for boundaries, targets, adversaries and sensing capabilities that may vary depending on operational need. This offers mission planners a means to rapidly respond to evolving environments, sensor capabilities and adversaries for autonomous air combat aircraft. It offers researchers access to operationally relevant aircraft physics. Tunnel code base is accessible to anyone familiar with Gymnasium and/or those with basic python skills. This paper includes a demonstration of a week long trade study that investigated a variety of training methods, observation spaces, and threat presentations. This enables increased collaboration between researchers and mission planners which can translate to a national military advantage. As warfare becomes increasingly reliant upon automation, software agility will correlate with decision advantages. Airmen must have tools to adapt to adversaries in this context. It may take months for researchers to develop skills to customize observation, actions, tasks and training methodologies in air combat simulators. In Tunnel, this can be done in a matter of days.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 42 references that are in the map. First posted on arXiv 4 May 2025.

Builds on

9 of 42 references.

References · 9

  1. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 523
  2. 2018Quantifying Generalization in Reinforcement Learningcited by 352
  3. 2020Liquid Time-constant Networkscited by 161
  4. 2022Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real worldcited by 56
  5. 2022Predictive Sampling: Real-time Behaviour Synthesis with MuJoCocited by 47
  6. 2024Deployable Reinforcement Learning with Variable Control Ratecited by 5
  7. 2024A survey of air combat behavior modeling using machine learningcited by 3
  8. 2022Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter Aircraftscited by 2
  9. 2022Automated Learning: An Implementation of The A* Search Algorithm over The Random Base Functionscited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat
  2. 2026BVR Sim: An Open and High-Throughput Environment for Heterogeneous Air-Combat Reinforcement Learning
  3. 2021Sim-Env: Decoupling OpenAI Gym Environments from Simulation Models
  4. 2023Learning Environment for the Air Domain (LEAD)
  5. 2025Fighter Jet Navigation and Combat using Deep Reinforcement Learning with Explainable AI
  6. 2025Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning
  7. 2026Robot Squid Game: Quadrupedal Locomotion for Traversing Narrow Tunnels
  8. 2026Perfecting Aircraft Maneuvers with Reinforcement Learning