In this paper, we present an advanced strategy for the coordinated control of a multi-agent aerospace system, utilizing Deep Neural Networks (DNNs) within a reinforcement learning framework. Our approach centers on optimizing autonomous task assignment to enhance the system's operational efficiency in object relocation tasks, framed as an aerospace-oriented pick-and-place scenario. By modeling this coordination challenge within a MuJoCo environment, we employ a deep reinforcement learning algorithm to train a DNN-based policy to maximize task completion rates across the multi-agent system. The objective function is explicitly designed to maximize effective object transfer rates, leveraging neural network capabilities to handle complex state and action spaces in high-dimensional aerospace environments. Through extensive simulation, we benchmark the proposed method against a heuristic combinatorial approach rooted in game-theoretic principles, demonstrating a marked performance improvement, with the trained policy achieving up to 16\% higher task efficiency. Experimental validation is conducted on a multi-agent hardware setup to substantiate the efficacy of our approach in a real-world aerospace scenario.

翻译：本文提出了一种多智能体航天系统协调控制的先进策略，该策略在强化学习框架内利用深度神经网络。我们的方法聚焦于优化自主任务分配，以提升系统在物体迁移任务中的运行效率，该任务被构建为航天导向的拾取-放置场景。通过在MuJoCo环境中对此协调挑战进行建模，我们采用深度强化学习算法训练基于深度神经网络的策略，以最大化多智能体系统的任务完成率。目标函数被明确设计为最大化有效物体转移率，利用神经网络的能力处理高维航天环境中的复杂状态与动作空间。通过大量仿真，我们将所提方法与基于博弈论原理的启发式组合方法进行基准测试，证明了其显著的性能提升，训练后的策略实现了高达16%的任务效率增益。实验验证在多智能体硬件平台上进行，以证实我们的方法在真实航天场景中的有效性。