Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2304.09669v1
• Date: Wed, 19 Apr 2023 13:54:37 GMT
• Title: Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach
• Authors: Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama
• Abstract summary: 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.
• Score: 0.2578242050187029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 based on rewards calculated using operational metrics. Also, through self-play experiments, it expects to generate new air combat tactics never seen before. Finally, we hope to examine a real pilot's ability, using virtual simulation, to interact in the same environment with the trained agent and compare their performances. This research will contribute to the air combat training context by developing agents that can interact with real pilots to improve their performances in air defense missions.

Related papers

• Autonomous Decision Making for UAV Cooperative Pursuit-Evasion Game with Reinforcement Learning [50.33447711072726]
  This paper proposes a deep reinforcement learning-based model for decision-making in multi-role UAV cooperative pursuit-evasion game. The proposed method enables autonomous decision-making of the UAVs in pursuit-evasion game scenarios.
  arXiv  Detail & Related papers  (2024-11-05T10:45:30Z)
• Exploring reinforcement learning for incident response in autonomous military vehicles [0.62914438169038]
  Research on this topic points to autonomous cyber defence as one of the capabilities that may be needed to accelerate the adoption of these vehicles for military purposes. Here, we explore reinforcement learning to train an agent that can autonomously respond to cyber attacks on unmanned vehicles in the context of a military operation. A key contribution of our work is demonstrating that reinforcement learning is a viable approach to train an agent that can be used for autonomous cyber defence on a real unmanned ground vehicle, even when trained in a simple simulation environment.
  arXiv  Detail & Related papers  (2024-10-28T18:08:23Z)
• 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)
• BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat [3.4311229392863463]
  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.
  arXiv  Detail & Related papers  (2024-03-26T09:39:21Z)
• Rethinking Closed-loop Training for Autonomous Driving [82.61418945804544]
  We present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents. We propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines.
  arXiv  Detail & Related papers  (2023-06-27T17:58:39Z)
• 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)
• Towards Cooperative Flight Control Using Visual-Attention [61.99121057062421]
  We propose a vision-based air-guardian system to enable parallel autonomy between a pilot and a control system. Our attention-based air-guardian system can balance the trade-off between its level of involvement in the flight and the pilot's expertise and attention.
  arXiv  Detail & Related papers  (2022-12-21T15:31:47Z)
• 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)
• Automating Privilege Escalation with Deep Reinforcement Learning [71.87228372303453]
  In this work, we exemplify the potential threat of malicious actors using deep reinforcement learning to train automated agents. We present an agent that uses a state-of-the-art reinforcement learning algorithm to perform local privilege escalation. Our agent is usable for generating realistic attack sensor data for training and evaluating intrusion detection systems.
  arXiv  Detail & Related papers  (2021-10-04T12:20:46Z)
• Learning Affordance Landscapes for Interaction Exploration in 3D Environments [101.90004767771897]
  Embodied agents must be able to master how their environment works. We introduce a reinforcement learning approach for exploration for interaction. We demonstrate our idea with AI2-iTHOR.
  arXiv  Detail & Related papers  (2020-08-21T00:29:36Z)

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.