We address the conflicting requirements of a multi-agent assignment problem
through constrained reinforcement learning, emphasizing the inadequacy of
standard regularization techniques for this purpose. Instead, we recur to a
state augmentation approach in which the oscillation of dual variables is
exploited by agents to alternate between tasks. In addition, we coordinate the
actions of the multiple agents acting on their local states through these
multipliers, which are gossiped through a communication network, eliminating
the need to access other agent states. By these means, we propose a distributed
multi-agent assignment protocol with theoretical feasibility guarantees that we
corroborate in a monitoring numerical experiment.

通过受限的强化学习解决多智能体分配问题中的冲突要求，强调标准正则化技术的不足，提出了一种状态增强方法，通过代理利用对偶变量的振荡来在任务之间交替，同时通过通信网络协调多智能体的动作，消除了访问其他智能体状态的需要，从而提出了一种具有理论可行性保证的分布式多智能体分配协议，并通过监视的数值实验予以证实。