# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 5 Attributes of Multi-Agent Deep Reinforcement Learning
### Applications  
The main applications of MADRL in the literature are as follows:  
* _Resource allocation and sharing_ enable agents to allocate and manage resources, such as distributed renewable energy sources (e.g., power grids) [55], and meet system demands in a distributed manner. In [35], in a wireless network, a node pair, which is a distributed agent, selects its operating channel for device-to-device (D2D) communication in a distributed manner. D2D enables neighboring nodes to communicate with each other directly without passing through a base station. MADRL can mitigate the instability of a multi-agent environment, whereby a centralized entity learns and shares learned knowledge with the node pairs, who subsequently make decisions independently. This helps to increase throughput. In [56], in a home network, a controller switches on or off home appliances based on their activities in the past 24 h and the user's presence to meet the user's resource (i.e., energy) demands and reduce resource consumption. MADRL addresses ultra-densification in which resources are limited in the presence of a large number of distributed agents. The centralized agent learns and performs action selection rather than the distributed agents, which also helps to increase energy efficiency. * _Traffic signal control in vehicular networks_ enables traffic signal controllers at intersections to manage and control traffic, such as selecting traffic phases (e.g., the north-south and west-east bounds) following traffic rules (e.g., vehicles cannot make a right turn) for reducing congestion at intersections [57; 58]. In [33], traffic signal controllers select their respective traffic phases in a distributed manner. MADRL addresses the ultra-densification and high dynamicity challenges using actors and critics that share knowledge (i.e., historical actions) with neighboring distributed agents cooperatively in order to select their respective actions. This helps to reduce the congestion level. * _Network routing_ enables source nodes to establish routes and send packets to destination nodes in networks with dynamically changing traffic patterns. In [6], MADRL addresses the ultra-densification, high dynamicity, and high dimension challenges by learning based on historical knowledge while making optimal decisions in route selection. This helps to reduce latency. * _Flood monitoring_ enables distributed agents to sense and monitor water levels based on real-time data to predict the complex flood levels [59; 60]. In [61], multiple aircraft update their respective positions in a distributed manner, enabling them to predict and report water levels at different locations. MADRL addresses the challenge of high dimension by enabling the distributed agents to share a common reward in a cooperative manner. This helps to increase the accumulated reward. * _Service migration_ enables intelligent devices, such as connected vehicles, to offload computing-intensive tasks (e.g., video and data processing, route planning, and traffic management in networks) from resource-limited distributed entities (e.g., vehicles and local servers) to centralized entities (e.g., edge servers at the edge of the network, and data centers) with extensive computing resources in order to achieve the expected service level. In [62], a service entity chooses whether or not to offload tasks to edge servers based on the utility (e.g., latency and energy) of a vehicle. MADRL addresses the ultra-densification and high dynamicity challenges by enabling distributed agents to share knowledge (i.e., historical actions) with neighboring distributed agents cooperatively in order to select their respective actions. This helps to reduce latency. * _Games_ enable agents to achieve the goals of games, such as winning the games or increasing human players' engagement. In [63], two players select their movements (i.e., up or down) in a pong game. MADRL addresses the challenge of high dynamicity using a common reward received after winning a game to encourage cooperative behavior among the players.