DMCN Nash Seeking Based on Distributed Approximate Gradient Descent Optimization Algorithms for MASs
针对多智能体多任务系统中任务分配与路径规划耦合导致的冲突问题，提出一种分布式混合合作-非合作模型及近似梯度下降算法，实现无冲突策略优化，并通过仿真验证了工程适用性。
A key problem in multiagent multitask systems is optimizing conflict-free strategies, especially when task-assignment is coupled with path-planning. Incomplete information exacerbates this complexity, leading to frequent conflicts, such as redundant agents performing the same task. Different from the existing single-type game model, this article introduces a distributed mixed cooperative-noncooperative (DMCN) model that considers nondifferentiable constraints. In order to deal with nondifferentiable task layer constraints, we use approximation operators and splitting schemes to transform the original optimization function into the primal-dual differentiable function. In order to obtain more stable solutions, a distributed approximate gradient descent optimization algorithm and conflict resolution mechanism are proposed, which enhances the convergence of our method. We use Lyapunov theory to verify the exponential convergence of the algorithm in the time range. Simulation and experiments demonstrate the superiority of this method and its applicability in engineering applications.