… In this article, we provide a survey of the role of the distributed approaches in DRL. … distributed
methods in DRL. We choose to overview these papers, from the perspective of distributed …

… To promote the research of deep reinforcement learning and try innovative distributed
algorithms in the field of EMSs, this paper used Deep Q-Network, Asynchronous Advantage Actor…

… multiple players multiple agents distributed deep reinforcement learning. Furthermore,
we … distributed deep reinforcement learning without many modifications of their non-distributed …

… a distributed deep reinforcement learning algorithm for the traffic light control problem,
which consists of local learning and global consensus. Firstly, the reinforcement learning …

… To search for the Nash equilibrium (NE) of the game, we adopt a method based on distributed
deep reinforcement learning. Also, the proposed method can preserve the privacy of the …

… distributed DRL techniques to efficiently perform in fog computing environments. Considering
the distributed … an EXperience-sharing Distributed Deep Reinforcement Learning-based …

… We present a study in Distributed Deep Reinforcement Learning (DDRL) focused on
scalability of a state-of-the-art Deep Reinforcement Learning algorithm known as Batch …

… deep-Q network-based algorithm combined with distributed … where the algorithm adopts
dueling deep network to learn the action-… Under the distributed coordinated learning manner and …

… To address this issue, we present a framework based on deep reinforcement learning (RL)
to train a deep neural network agent that controls a model solve by varying parameters …

… proposes a distributed deep reinforcement … deep RL approach in which each agent learns
a policy requiring only local information. We show with Monte Carlo simulations that the deep …