A Pursuit Strategy for Multi-Agent Pursuit-Evasion Game via Multi-Agent Deep Deterministic Policy Gradient Algorithm

This paper studies a classical pursuit-evasion problem. The pursuer attempts to capture the faster evader in a bounded area. The velocity of evader is 1.2 times as fast as the pursuers'. All of them have adaptive strategies. We use game theory to model the multi-agent pursuit-evasion game and p...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings of ... IEEE International Conference on Unmanned Systems (Online) s. 418 - 423
Hlavní autoři: Ye, Jianfeng, Wang, Qing, Ma, Bei, Wu, Yongbao, Xue, Lei
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 28.10.2022
Témata:
Adaptation models
adaptive strategy
Convergence
Games
MADDPG
multi-agent
Nash equilibrium
Neural networks
Training
ISSN:2771-7372
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper studies a classical pursuit-evasion problem. The pursuer attempts to capture the faster evader in a bounded area. The velocity of evader is 1.2 times as fast as the pursuers'. All of them have adaptive strategies. We use game theory to model the multi-agent pursuit-evasion game and prove that the game model has Nash equilibrium. Then, we modify the multi-agent deep deterministic policy gradient (MADDPG) algorithm for seeking the Nash equilibrium. The simulation examples are given to illustrate the effectiveness of the designed method.
ISSN:2771-7372
DOI:10.1109/ICUS55513.2022.9986838