Distributed Nonconvex Optimization and Application to UAV Optimal Rendezvous Formation
提出一种带理论保证的分布式多智能体深度强化学习算法，解决分布式非凸约束优化问题，并通过数值仿真和实验验证其在无人机最优会合编队中的有效性。
A distributed multiagent deep reinforcement learning algorithm (DMADRLA) with theoretical guarantees is proposed for the distributed nonconvex constraint optimization problem. This algorithm provides an innovative theoretical framework for distributed nonconvex optimization problems (DNCOPs) by combining traditional distributed constraint optimization and multiagent deep reinforcement learning methods. This combination eliminates the need for general assumptions on the cost function, enabling a more comprehensive view of distributed nonconvex optimization strategies. It allows for the analysis of both traditional distributed constrained optimization and multiagent deep reinforcement learning methods in one unified approach. Finally, the effectiveness of the algorithm is verified through numerical simulations and experimental verification.