Deep reinforcement learning and its application in autonomous fitting optimization for attack areas of UCAVs

Li, Yue; Qiu, Xiaohui; Liu, Xiaodong; Xia, Qing

doi:10.23919/jsee.2020.000048

Cited by 25 publications

(7 citation statements)

References 1 publication

(1 reference statement)

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“…The strength of deep learning lies in perception, and reinforcement learning has great advantages in decision-making applications [7]. Deep reinforcement learning combines the perception ability of deep learning and the decision-making ability of reinforcement learning [8], and comprehensively uses deep reinforcement learning and deduction. A large amount of data provided by the system to understand the battlefield situation and conduct situational deduction based on this is the current mainstream research direction.…”

Section: Research On Key Technologies Of Auxiliary Decision-makingmentioning

confidence: 99%

Research on auxiliary decision-making for sea striking of naval aviation based on deep reinforcement learning

Wu,

Yin

2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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The situation of the future naval battlefield will become more and more complex, and it will become a trend to develop various military auxiliary decision-making systems based on artificial intelligence and big data technology. This paper sorts out the key technologies of the auxiliary decision-making system based on deep reinforcement learning. On this basis, it proposes the construction method of the naval aviation sea-striking agent model, and completes the construction of the training framework with the combat deduction system as the environment. Finally, it summarizes and prospects some of future work.

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Section: Research On Key Technologies Of Auxiliary Decision-makingmentioning

confidence: 99%

Research on auxiliary decision-making for sea striking of naval aviation based on deep reinforcement learning

Wu,

Yin

2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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“…Referring from Figure 3, fx go , z go g is the approximate distance to interceptor, and fxf go , zf go g is for target. Equation (20)…”

Section: Mdp-based Maneuver Decision Modelingmentioning

confidence: 99%

“…18 Li et al 19 also addressed the application of DRL to optimize online flight cruise control of UAV. Li et al 20 solved the guidance problem of cluster vehicles via DQN, and the literature ensured the target resource allocation with availability and effectiveness. Existing literature mainly focus on trajectory planning and guidance control, and rarely literature was involved in the penetration problem of vehicle under the attack–defense confrontation model.…”

Section: Introductionmentioning

confidence: 99%

High-dynamic intelligent maneuvering guidance strategy via deep reinforcement learning

Zhao

Zhu

Bao

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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Aiming at the coordination between maneuvering penetration and high-precision guidance under complex flight missions of high-velocity vehicle, the manuscript studies a three-dimensional high-dynamic intelligent maneuvering guidance strategy based on optimal control and deep reinforcement learning (DRL). A three-dimensional attack–defense model is established, and maneuver guidance mission is decomposed into longitudinal and lateral directions. In the longitudinal direction, maneuvering model with the instantaneous miss distance as the control variable is constructed, and the maximum value principle is employed to obtain the optimal maneuver duration and start timing. In the lateral direction, Markov decision process model of maneuver guidance is proposed by synthesizing the guidance error and miss distance of encounter point, and the reward function is designed by considering maneuver and guidance performance. The DRL method is used to learn and train the maneuver strategy, and the training process is improved as well. The simulation results show that the intelligent maneuvering guidance strategy can improve the penetration performance, reduce influence of maneuver flight on the guidance accuracy, and ensure the adaptability under changeable flight missions.

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“…e application of RL also plays a great role in the path control of UAV and self-driving vehicles. Zeng et al, Yang et al, and Li et al have pointed out that the movement or task execution process of UAV is a continuous control problem in a changing environment, and RL and deep deterministic policy gradient in DRL can be used to better realize the control process of UAV [24][25][26]. Compared to the path control of UAV, driverless vehicle in recent years is more complicated and has a more complex environment.…”

Section: Reinforcement Learning In Path Planningmentioning

confidence: 99%

A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization

Nai¹,

Yang²,

Lin³

et al. 2022

Journal of Mathematics

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The transportation system of those countries has a huge traffic flow is bearing great pressure on transportation planning and management. Vehicle path planning is one of the effective ways to alleviate such pressure. Deep reinforcement learning (DRL), as a state-of-the-art solution method in vehicle path planning, can better balance the ability and complexity of the algorithm to reflect the real situation. However, DRL has its own disadvantages of higher search cost and earlier convergence to the local optimum, as vehicle path planning issues are usually in a complex environment, and their action set can be diverse. In this paper, a mixed policy gradient actor-critic (AC) model with random escape term and filter operation is proposed, in which the policy weight is both data driven and model driven. The empirical data-driven method is used to improve the poor asymptotic performance, and the model-driven method is used to ensure the convergence speed of the whole model. At the same time, in order to avoid the model converging local optimum, a random escape term has been added in the policy weight update process to overcome the problem that it is difficult to optimize the non-convex loss function, and the random escape term can help to explore the policy in more directions. In addition, filter optimization has been innovatively introduced in this paper, and the step size of each iteration of the model is selected through the filter optimization algorithm to achieve the better iterative effect. Numerical experiment results have shown that the model proposed in this paper can not only improve the accuracy of the solution without losing the accuracy but also speed up the convergence speed and improve the utilization of data.

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Deep reinforcement learning and its application in autonomous fitting optimization for attack areas of UCAVs

Cited by 25 publications

References 1 publication

Research on auxiliary decision-making for sea striking of naval aviation based on deep reinforcement learning

Research on auxiliary decision-making for sea striking of naval aviation based on deep reinforcement learning

High-dynamic intelligent maneuvering guidance strategy via deep reinforcement learning

A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization

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