… game (SG) guided multi-agent deep reinforcement learning (MADRL) approach, which …
Our proposed Stackelberg game guided multi-agent deep reinforcement learning approach for …

… grid charging environment that requires quick decisions. Therefore, we propose a multi-agent
deep reinforcement learning approach with a centralized training and decentralized …

… deep reinforcement learning (RL) using a grid map of the farm. It is achieved by modeling the
experience of the robot using the Markov Decision … , which is a deep reinforcement learning …

… The advent of deep reinforcement learning (DRL) provides a new attractive solution for this
… decision-making network for cognitive radar via multi-agent deep reinforcement learning (…

… 4.4 Markov Decision Process Setup We formulate our problem as a multi-agent Markov
Decision Process (MDP) with N agents (households). We assume the optimization horizon is one …

… in the field of multi-agent deep reinforcement learning. We … deep reinforcement learning
methods with a multi-agent … decision process as a framework for single-agent learning in …

… In industry, deep reinforcement learning (DRL) is increasingly used in robotics, job shop …
security aspects, multi-agent systems (MASs) are preferred for decentralized decision-making. …

… , which is a multi-agent extension of Markov decision process. Then, we propose a model-free
control algorithm to solve the Markov game in Section III based on multi-agent DRL. Since …

… Thus, we need computers to be able to learn to make a reasonable decision by interacting
with the environment. In recent years, with the sustainable development of the Internet and …

… Fortunately, artificial intelligence (AI), particularly deep reinforcement learning (DRL), has
been proved to be an effective method to make clever decisions under the uncertain …