无人机协同追逃博弈中的强化学习自主决策
Autonomous Decision Making for UAV Cooperative Pursuit-Evasion Game with Reinforcement Learning
AI总结：
本文提出基于深度强化学习的多无人机协同追逃博弈决策模型，采用多环境异步双深度Q网络与优先级经验回放算法，通过角色与目标分配提升协作能力，仿真验证了自主决策与协作性能。
摘要：
智能决策在无人机（UAV）中的应用日益增多，随着无人机1v1追逃博弈的发展，多无人机协同博弈已成为新的挑战。本文提出一种基于深度强化学习的多角色无人机协同追逃博弈决策模型，以解决在复杂博弈环境中使无人机能够自主决策的挑战。为了提高强化学习算法在具有高维状态-动作空间的无人机追逃博弈环境中的训练效率，本文提出多环境异步双深度Q网络与优先级经验回放算法，以有效训练无人机的博弈策略。此外，为提高协作能力和任务完成效率，并最小化无人机在追逃博弈中的成本，本文重点研究多无人机环境中的角色与目标分配。通过在不同场景中为无人机分配不同的任务和角色，获得了具有不同无人机数量的协同博弈决策模型。仿真结果表明，所提方法使无人机能够在追逃博弈场景中实现自主决策，并展现出显著的协作能力。
英文摘要：
The application of intelligent decision-making in unmanned aerial vehicle (UAV) is increasing, and with the development of UAV 1v1 pursuit-evasion game, multi-UAV cooperative game has emerged as a new challenge. This paper proposes a deep reinforcement learning-based model for decision-making in multi-role UAV cooperative pursuit-evasion game, to address the challenge of enabling UAV to autonomously make decisions in complex game environments. In order to enhance the training efficiency of the reinforcement learning algorithm in UAV pursuit-evasion game environment that has high-dimensional state-action space, this paper proposes multi-environment asynchronous double deep Q-network with priority experience replay algorithm to effectively train the UAV's game policy. Furthermore, aiming to improve cooperation ability and task completion efficiency, as well as minimize the cost of UAVs in the pursuit-evasion game, this paper focuses on the allocation of roles and targets within multi-UAV environment. The cooperative game decision model with varying numbers of UAVs are obtained by assigning diverse tasks and roles to the UAVs in different scenarios. The simulation results demonstrate that the proposed method enables autonomous decision-making of the UAVs in pursuit-evasion game scenarios and exhibits significant capabilities in cooperation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
