mlsanityData to 14 Oct 2026

BVR Sim: An Open and High-Throughput Environment for Heterogeneous Air-Combat Reinforcement Learning

arXiv 2608.25419 ↗First posted 26 Aug 2026cs.MA cs.LGcited by 0

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

Abstract (arXiv)

Beyond-visual-range (BVR) air combat is a challenging reinforcement-learning domain characterized by partial observability, long-horizon decision making, energy management, and limited weapons. We present BVR Sim, an open-source Gymnasium-style environment designed for heterogeneous air-combat reinforcement learning. BVR Sim supports multiple JSBSim aircraft models, including the F-15, F-16, F/A-18, and F-22, with configurable weapons, sensors, controllers, and opponents. A unified tactical action interface specifies desired heading, altitude, speed, and weapon release above aircraft-specific inner-loop controllers, enabling policies to operate across heterogeneous platforms. The environment provides interchangeable Python and accelerated C++ backends, entity-oriented observations, compositional rewards, scripted opponents, replay and visualization, and adapters for multi-agent learning frameworks. At a 0.4-s decision interval, the C++ backend achieves 104 simulated seconds per wall-clock second in 1-vs-1 and remains practical through 10-vs-10 scenarios. A policy trained only on the F-16 transfers without retraining to four unseen aircraft, reaching a 45.5% mean win rate with aircraft-specific controller adaptation. MAPPO and HAPPO experiments further verify end-to-end compatibility with standard multi-agent reinforcement-learning pipelines.

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 7 of its 12 references that are in the map. First posted on arXiv 26 Aug 2026.

Builds on

7 of 12 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Light Aircraft Game : Basic Implementation and training results analysis17 Jun
  2. 2024BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat26 Mar

Other references · 5

  1. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  2. 2019The StarCraft Multi-Agent Challengecited by 597
  3. 2021Trust Region Policy Optimisation in Multi-Agent Reinforcement Learningcited by 186
  4. 2020FACMAC: Factored Multi-Agent Centralised Policy Gradientscited by 178
  5. 2023Heterogeneous-Agent Reinforcement Learningcited by 69

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  5. 2024A survey of air combat behavior modeling using machine learning
  6. 2026Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat
  7. 2024An Imitative Reinforcement Learning Framework for Pursuit-Lock-Launch Missions
  8. 2024Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning