Abstract Machine scheduling aims to optimally assign jobs to a single or a group of
machines while meeting manufacturing rules as well as job specifications. Optimizing the …

This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything
(C-V2X) communication to enable vehicles to learn communication parameters in …

In this study, we studied the Unmanned System Group (USG) Autonomous Collaborative
Combat Strategy (ACCS) and the Parallel Decoupling-Multi-agent Deep Deterministic Policy …

Smart grids are a reality in distribution systems. They have assisted in the operation, control,
and most of all, the protection of urban networks, significantly solving the contingencies of …

Intrusion detection is a crucial task in the field of computer security as it helps protect these
systems against malicious attacks. New techniques have been developed to cope with the …

In the multi-agent offensive and defensive game (ODG), each agent achieves its goal by
cooperating or competing with other agents. The multi-agent deep reinforcement learning …

Network slices are generally coupled with services and face service continuity/unavailability
concerns due to the high mobility and dynamic requests from users. Network slice mobility …

This paper introduces a novel multi-agent Deep Reinforcement Learning (DRL) framework
named the Cooperative Dual-Actor Proximal Policy Optimization (CDA-PPO) algorithm …

Food–energy–water (FEW) nexus analyses are critical to sustainable development. Nexus
analyses form a unique multiagent decision-making arena that requires using a system …

Multi-agent Deep Reinforcement Learning (MADRL) has been applied to a plethora of state-
of-the-art applications such as resource allocations and network routing in both centralized …