Decentralized partially observable Markov decision process (DEC-POMDP) models sequential decision making problems by a team of agents. Since the planning of DEC-POMDP can be interpreted as the maximum likelihood estimation for the latent variable mod...
Decentralized partially observable Markov decision processes (Dec-POMDPs) are general multi-agent models for planning under uncertainty, but are intractable to solve. Doubly exponential growth of the search space as the horizon increases makes a brut...
Coordinating multiple unmanned aerial vehicles (UAVs) for the purposes of target finding or surveying points of interest in large, complex, and partially observable environments remains an area of exploration. This work proposes a modeling approach a...
With the development of value decomposition methods, multi-agent reinforcement learning (MARL) has made significant progress in balancing autonomous decision making with collective cooperation. However, the collaborative dynamics among agents are con...
Directional unmanned aerial vehicle (UAV) ad hoc networks (DUANETs) are widely applied due to their high flexibility, strong anti-interference capability, and high transmission rates. However, within directional networks, complex mutual interference...
Considering that the unmanned aerial vehicles (UAVs) are susceptible to both co-channel interference and malicious jamming with limited onboard battery energy, this paper proposes an energy-harvesting-assisted anti-jamming communication framework for...
Uncertainty remains a central challenge in robotic navigation, exploration, and coordination. This paper examines how Partially Observable Markov Decision Processes (POMDPs) and their decentralized variants (Dec-POMDPs) provide a rigorous foundation...
With the continuous expansion of data centers, their carbon emission has become a serious issue. A number of studies are committing to reduce the carbon emission of data centers. Carbon trading, carbon capture, and power-to-gas technologies are promi...
Multi-robot systems (MRSs) offer distinct advantages in large-scale exploration but require tight coupling between decentralized decision-making and collaborative estimation. This survey reviews learning-based multi-robot Active Collaborative Simulta...
Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-vehicle pursuit (MVP) games, a multi-vehicle cooperative ability to capture...
Unmanned Aerial Vehicles (UAVs) have garnered widespread attention in reconnaissance and search operations due to their low cost and high flexibility. However, when multiple UAVs (multi-UAV) collaborate on these tasks, a limited communication range c...
High penetration of distributed photovoltaic (PV) generation in active distribution networks (ADNs) has intensified voltage violations and rapid voltage fluctuations, especially under extreme reverse-power-flow conditions. Traditional centralized vol...
Unmanned aerial vehicles (UAVs) furnished with computational servers enable user equipment (UE) to offload complex computational tasks, thereby addressing the limitations of edge computing in remote or resource-constrained environments. The applicati...
UAV, mounted aerial base stations offer remarkable deployment flexibility for 6G Non Terrestrial Networks (NTNs), yet their hovering energy demands present a critical sustainability bottleneck, particularly as network topologies continuously evolve i...
The merging behavior of vehicles at entry ramps and the speed differences between ramps and mainline traffic cause merging traffic bottlenecks. Current research, primarily focusing on single traffic control strategies, fails to achieve the desired ou...
To address active voltage control in photovoltaic (PV)-integrated distribution networks characterized by weak voltage support conditions, this paper proposes a multi-agent deep reinforcement learning (MADRL)-based coordinated control method for PV cl...
Multi-UAV Cooperative Target Search (MCTS) is a critical task in low-altitude sensing applications, requiring agents to efficiently explore unknown environments under complex constraints. However, traditional search methods are mostly unscalable and...
To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrat...
The increasing penetration of inverter-interfaced photovoltaic (PV) generation in active distribution networks (ADNs) intensifies fast voltage violations and makes real-time Volt-VAR control (VVC) challenging, especially when each inverter has only p...
The growing penetration of variable renewable energy sources transforms power grid dispatch into a high-dimensional, stochastic, and multi-agent decision-making problem that challenges both classical optimization and standard Reinforcement Learning (...
Intelligent Connected Vehicles (ICVs) impose stringent requirements on real-time computational services. However, limited onboard resources and the high latency of remote cloud servers restrict traditional solutions. Unmanned Aerial Vehicle (UAV)-ass...
As an emerging direction of multi-agent collaborative control technology, multiple autonomous underwater vehicle (multi-AUV) cooperative area search technology has played an important role in civilian fields such as marine resource exploration and de...
Rapid localisation of trapped victims after urban disasters is essential but challenging because Bluetooth Low Energy (BLE) beacons are intermittent, radio propagation is obstructed by rubble, UAVs are energy-constrained, and real-world multi-UAV tra...
Shared immersive environment sports venues, virtual classrooms, and collaborative workspaces require multiple users to stream 360° videos simultaneously over the same edge network, yet every existing adaptive bitrate system optimises each viewer...