… a learning agent and each resource allocation solution corresponds to an action taken by the
UAVs. Afterwards, we develop a multi-agent reinforcement learning (… using learning. More …

… of deep reinforcement learning (DRL) for joint subcarrier-power allocation to achieve low …
) networks, where each subcarrier can be allocated to multiple D2D pairs and each D2D pair is …

… allocation problem in wireless networks [32], [33], here, deep reinforcement learning was
used to learn … A multiagent dynamic resource allocation algorithm has been proposed for multi-…

… To efficiently provide on-demand resource access, the macro … allocate proper amounts of
resources to vehicles. Since there is no central controller, we formulate the resource allocation …

… selection at the edge level, and resource allocation at the Radio Access Network (RAN)
level… In particular, our framework employs a deep Multi-Agent Reinforcement Learning (DMARL) …

… resource allocation model based on an intelligent multi-agent system and reinforcement
learning … It combines the multi-agent characteristics and the Q-learning process to improve the …

… In industry, deep reinforcement learning (DRL) is increasingly used in robotics, job shop …
resource allocation for industrial edge devices. An optimal usage of available resources of the …

… Abstract—We propose a mechanism for distributed resource management and interference
mitigation in wireless networks using multi-agent deep reinforcement learning (RL). We …

… and distributed deep reinforcement learning-based resource allocation approaches, we
propose a multi-agent deep deterministic policy gradient algorithm with centralized learning and …

… In this paper, we proposed a multi-agent RL solution for resource allocation and trajectory
design in a multi-UAV-aided cellular network with user locations and channel parameters …