… solve the multi-agent task and get decentralized policies for each UE, we develop a multi-agent
reinforcement learning (MARL) algorithm based on the proximal policy optimization (PPO…

… (STAPP) based on a multi-agent deep deterministic policy gradient (… of multi-agent
reinforcement learning algorithm. In STAPP, the MUTAPP problem is first constructed as a …

… Therefore, a joint optimization technique that optimizes IT and cooling systems co… In this
paper, a joint optimization framework, MACEEC (MultiAgent deep reinforcement learning-based …

… on two multi-agent environment suites: a discrete version of the multiple-particle environment
(MPE) (Lowe et al., 2017; Wang et al., 2020) and the Starcraft multi-agent challenge (SMAC…

… We jointly optimize the throughput, the transmission delay, and the … multi-agent deep
deterministic policy gradient (IMADDPG) algorithm, having the characteristics of centralized training …

… of reinforcement learning methods to multiple agents [17]. Guestrin et al. later extended this
idea and factored the joint … used message passing to find the joint optimal actions [18]. Lauer …

… We aim to jointly optimize the geographical fairness among all … The above optimization
problem includes both integer and … , a Multi-Agent deep reinforcement learning based Trajectory …

… a multi-agent deep reinforcement learning (MADRL) approach, ie, multi-agent deep
deterministic policy gradient (MADDPG) to maximize the secure capacity by jointly optimizing the …

… this progress, multi-agent reinforcement learning gains rapid traction, … in the field of multi-agent
deep reinforcement learning. We … deep reinforcement learning methods with a multi-agent …

… multi-agent reinforcement learning method, named KS-DDPG (Knowledge Sharing Deep
Deterministic Policy Gradient) to achieve optimal … -of-the-art reinforcement learning-based and …