mlsanityData to 14 Oct 2026

Interpretable DRL-based Maneuver Decision of UCAV Dogfight

arXiv 2407.01571 ↗First posted 28 May 2024cs.RO cs.LGcited by 0

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

Abstract (arXiv)

This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT. Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents. Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved. The agent performs yo-yo to adjust its turn rate and gain higher maneuverability. Emergence of "Dive and Chase" behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent.

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 3 of its 18 references that are in the map. First posted on arXiv 28 May 2024.

Builds on

3 of 18 references.

References · 3

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2022A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combatcited by 10
  3. 2021Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trialscited by 8

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

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  4. 2024Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning
  5. 2025Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness
  6. 2020Deep Reinforcement Learning based Local Planner for UAV Obstacle Avoidance using Demonstration Data
  7. 2024Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning
  8. 2024An Imitative Reinforcement Learning Framework for Pursuit-Lock-Launch Missions