The field of multi-agent systems is moving towards decentralized control and task offloading, with a focus on developing innovative solutions that can operate effectively in complex environments with limited communication and sensing capabilities. Recent research has explored the use of deep learning-based approaches, such as VariAntNet, to facilitate agent swarming and collaborative task execution. Other works have investigated the use of decentralized frameworks, like the one proposed for task offloading in wireless edge networks, which enables agents to align with global resource usage objectives while requiring little direct communication.

Noteworthy papers in this area include: Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks, which proposes a decentralized framework for task offloading and establishes theoretical guarantees under mild assumptions. VariAntNet: Learning Decentralized Control of Multi-Agent Systems, which demonstrates a deep learning-based decentralized control model that significantly outperforms an existing analytical solution in terms of convergence rate and swarm connectivity. Event Driven CBBA with Reduced Communication, which introduces an event-driven communication mechanism that reduces message transmissions by up to 52% while maintaining the convergence and performance bounds of the consensus-based bundle algorithm.