Abstract Cross-device Federated Learning (FL) faces significant challenges where low-end
clients that could potentially make unique contributions are excluded from training large …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

This paper explores how Large Language Models (LLMs) can automate consensus-seeking
in supply chain management (SCM), where frequent decisions on problems such as …

Multi-agent reinforcement learning (MARL) is employed to develop autonomous agents that
can learn to adopt cooperative or competitive strategies within complex environments …

Supply networks require collaboration in a competitive environment. To achieve this, nodes
in the network often form symbiotic relationships as they can be adversely effected by the …

With the integration of large-scale renewable energy units into the grid, their intrinsic low
inertia characteristics result in a decrease in the inertia level at the node hierarchy …

Abstract Multi-agent Reinforcement Learning (MARL) has shown significant success in
solving large-scale complex decision-making problems in multi-agent systems (MAS) while …

The rise of e-commerce demands greater efficiency in warehouses, requiring dynamic task
allocation among humans and robots. Traditional methods often fail in such complex …

In order to cope with global climate challenges and the depletion of fossil energy, the
penetration rate of asynchronous power sources has gradually increased, resulting in further …

Large-scale influx of renewable energy into current electric power grid increases risks
regarding frequency instability and makes power grid peak-shaving knotty. The solution lies …