UAV Target Tracking Method Based on Deep Reinforcement Learning

Zhang, Haohui; He, Pingkuan; Zhang, Ming; Chen, Daqing; Neretin, E S; Li, Bo

doi:10.1109/iccsi55536.2022.9970588

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The four meta-policies all use the same state space, which is represented by nine tuples, as shown in Formula (6).…”

Section: Meta-policy Model Training Algorithm Based On Sacmentioning

confidence: 99%

“…Unmanned aerial vehicles (UAVs) [1][2][3][4][5][6][7] are used in many fields, such as intelligent confrontation [8], target rounding [9] and intelligent transportation [10], because of their characteristics of being unmanned, having good concealment and having no casualties. UAV pursuit-evasion [11] involves a game between two UAVs with competing interests.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Maneuver Decision Method Based on PG-Option for UAV Pursuit-Evasion Game

Zhang

et al. 2023

Drones

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Aiming at the autonomous decision-making problem in an Unmanned aerial vehicle (UAV) pursuit-evasion game, this paper proposes a hierarchical maneuver decision method based on the PG-option. Firstly, considering various situations of the relationship of both sides comprehensively, this paper designs four maneuver decision options: advantage game, quick escape, situation change and quick pursuit, and the four options are trained by Soft Actor-Critic (SAC) to obtain the corresponding meta-policy. In addition, to avoid high dimensions in the state space in the hierarchical model, this paper combines the policy gradient (PG) algorithm with the traditional hierarchical reinforcement learning algorithm based on the option. The PG algorithm is used to train the policy selector as the top-level strategy. Finally, to solve the problem of frequent switching of meta-policies, this paper sets the delay selection of the policy selector and introduces the expert experience to design the termination function of the meta-policies, which improves the flexibility of switching policies. Simulation experiments show that the PG-option algorithm has a good effect on UAV pursuit-evasion game and adapts to various environments by switching corresponding meta-policies according to current situation.

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“…The four meta-policies all use the same state space, which is represented by nine tuples, as shown in Formula (6).…”

Section: Meta-policy Model Training Algorithm Based On Sacmentioning

confidence: 99%