# Interpretable DRL-based Maneuver Decision of UCAV Dogfight

arXiv: https://arxiv.org/abs/2407.01571 · HTML: https://mlsanity.com/paper/2407.01571/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 3 of 3)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat](https://mlsanity.com/paper/2212.03830/) (2022)
- [Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials](https://mlsanity.com/paper/2105.00990/) (2021)

## 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.