… ’ network formation optimizes the multi-hop UAV network topology to minimize the energy
consumption and transmission delay. The joint network formation … adaptive network formation …

… deep reinforcement learning for routing in AANETs aiming at minimizing the end-to-end (E2E)
delay. Specifically, a deep Q-network … ’s dynamics by using a deep value network (DVN) …

… networks (RDN) when the utility power is unavailable. This paper proposes a RDN-oriented
microgrid formation (RoMF) method based on the deep reinforcement … microgrid formation …

… -interested, the gains from team formation must be allocated … and form teams using deep
reinforcement learning. Importantly… spatially extended team-formation negotiation environments, …

… This paper proposes a new deep reinforcement learning (RL) based model-free on-line
dynamic MMGF scheme. The dynamic MMGF problem is formulated as a Markov decision …

… In this paper, an intelligent decision-making UAV formation information sharing mechanism
that uses reinforcement learning without any prior knowledge is designed. The interaction …

… A feedforward deep neural network or multilayer … formed by composing many simpler
functions at each layer. A convolutional neural network (CNN) is a feedforward deep neural network…

… , a deep reinforcement learning (RL) approach to solve the network planning problem. This
… (GNN) and a novel domain-specific node-link transformation for state encoding, in order to …

… (pattern formation) from any initial configuration. We propose a decentralized multi-agent
deep reinforcement learning architecture MAPF-DQN (Multi-Agent Pattern Formation DQN) in …

… sensors and the concrete formation we investigated. Keeping this … -to-end deep reinforcement
learning approach for formation … in order to stay in the desired formation. In contrast to our …