BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat

• URL: http://arxiv.org/abs/2403.17533v1
• Date: Tue, 26 Mar 2024 09:39:21 GMT
• Title: BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat
• Authors: Edvards Scukins, Markus Klein, Lars Kroon, Petter Ögren,
• Abstract summary: We create a reinforcement learning environment to help investigate potential air combat tactics. Long-range missiles are often the first weapon to be used in aerial combat. This article describes the building blocks of the environment and some use cases.
• Score: 3.4311229392863463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Creating new air combat tactics and discovering novel maneuvers can require numerous hours of expert pilots' time. Additionally, for each different combat scenario, the same strategies may not work since small changes in equipment performance may drastically change the air combat outcome. For this reason, we created a reinforcement learning environment to help investigate potential air combat tactics in the field of beyond-visual-range (BVR) air combat: the BVR Gym. This type of air combat is important since long-range missiles are often the first weapon to be used in aerial combat. Some existing environments provide high-fidelity simulations but are either not open source or are not adapted to the BVR air combat domain. Other environments are open source but use less accurate simulation models. Our work provides a high-fidelity environment based on the open-source flight dynamics simulator JSBSim and is adapted to the BVR air combat domain. This article describes the building blocks of the environment and some use cases.

Related papers

• An Imitative Reinforcement Learning Framework for Autonomous Dogfight [20.150691753213817]
  Unmanned Combat Aerial Vehicle (UCAV) dogfight plays a decisive role on the aerial battlefields. This paper proposes a novel imitative reinforcement learning framework, which efficiently leverages expert data while enabling autonomous exploration. The proposed framework can learn a successful dogfight policy of 'pursuit-lock-launch' for UCAVs.
  arXiv  Detail & Related papers  (2024-06-17T13:59:52Z)
• Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach [0.2578242050187029]
  This work contributes to developing an agent based on deep reinforcement learning capable of acting in a beyond visual range (BVR) air combat simulation environment. The paper presents an overview of building an agent representing a high-performance fighter aircraft that can learn and improve its role in BVR combat over time. It also hopes to examine a real pilot's ability, using virtual simulation, to interact in the same environment with the trained agent and compare their performances.
  arXiv  Detail & Related papers  (2023-04-19T13:54:37Z)
• Reinforcement Learning Based Self-play and State Stacking Techniques for Noisy Air Combat Environment [1.7403133838762446]
  The complexity of air combat arises from aggressive close-range maneuvers and agile enemy behaviors. In this study, we developed an air combat simulation, which provides noisy observations to the agents. We present a state stacking method for noisy RL environments as a noise reduction technique.
  arXiv  Detail & Related papers  (2023-03-06T12:23:23Z)
• The eyes and hearts of UAV pilots: observations of physiological responses in real-life scenarios [64.0476282000118]
  In civil and military aviation, pilots can train themselves on realistic simulators to tune their reaction and reflexes. This work aims to provide a solution to gather pilots behavior out in the field and help them increase their performance.
  arXiv  Detail & Related papers  (2022-10-26T14:16:56Z)
• Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter Aircrafts [0.0]
  We present a semi-realistic flight simulation environment Harfang3D Dog-Fight Sandbox for fighter aircrafts. It is aimed to be a flexible toolbox for the investigation of main challenges in aviation studies using Reinforcement Learning. Software also allows deployment of bot aircrafts and development of multi-agent tasks.
  arXiv  Detail & Related papers  (2022-10-13T18:18:09Z)
• Learning a Single Near-hover Position Controller for Vastly Different Quadcopters [56.37274861303324]
  This paper proposes an adaptive near-hover position controller for quadcopters. It can be deployed to quadcopters of very different mass, size and motor constants. It also shows rapid adaptation to unknown disturbances during runtime.
  arXiv  Detail & Related papers  (2022-09-19T17:55:05Z)
• E^2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles [78.50123964690094]
  Unmanned Aerial Vehicles (UAVs) based video text spotting has been extensively used in civil and military domains. Our proposed energy-efficient video text spotting solution, dubbed as E2VTS, outperforms all previous methods by achieving a competitive tradeoff between energy efficiency and performance.
  arXiv  Detail & Related papers  (2022-06-05T22:43:17Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• RMA: Rapid Motor Adaptation for Legged Robots [71.61319876928009]
  This paper presents Rapid Motor Adaptation (RMA) algorithm to solve this problem of real-time online adaptation in quadruped robots. RMA is trained completely in simulation without using any domain knowledge like reference trajectories or predefined foot trajectory generators. We train RMA on a varied terrain generator using bioenergetics-inspired rewards and deploy it on a variety of difficult terrains.
  arXiv  Detail & Related papers  (2021-07-08T17:59:59Z)
• Do You See What I See? Coordinating Multiple Aerial Cameras for Robot Cinematography [9.870369982132678]
  We develop a real-time multi-UAV coordination system that is capable of recording dynamic targets while maximizing shot diversity and avoiding collisions. We show that our coordination scheme has low computational cost and takes only 1.17 ms on average to plan for a team of 3 UAVs over a 10 s time horizon.
  arXiv  Detail & Related papers  (2020-11-10T22:43:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.