Deep convolutional adversarial graph autoencoder using positive pointwise mutual information for graph embedding

Graph embedding aims to map the high-dimensional nodes to a low-dimensional space and learns the graph relationship from its latent representations.Most existing graph embedding methods focus on the topological structure of graph data,but ignore the semantic information of graph data,which results in the unsatisfied performance in practical applications.To overcome the problem,this paper proposes a novel deep convolutional adversarial graph autoencoder(GAE)model.To embed the semantic information between nodes in the graph data,the random walk strategy is first used to construct the positive pointwise mutual information(PPMI)matrix,then,graph convolutional net-work(GCN)is employed to encode the PPMI matrix and node content into the latent representation.Finally,the learned latent representation is used to reconstruct the topological structure of the graph data by decoder.Furthermore,the deep convolutional adversarial training algorithm is introduced to make the learned latent representation conform to the prior distribution better.The state-of-the-art experimental results on the graph data validate the effectiveness of the proposed model in the link prediction,node clustering and graph visualization tasks for three standard datasets,Cora,Citeseer and Pubmed.

Improving multi-target cooperative tracking guidance for UAV swarms using multi-agent reinforcement learning

Multi-Target Tracking Guidance(MTTG)in unknown environments has great potential values in applications for Unmanned Aerial Vehicle(UAV)swarms.Although Multi-Agent Deep Reinforcement Learning(MADRL)is a promising technique for learning cooperation,most of the existing methods cannot scale well to decentralized UAV swarms due to their computational complexity or global information requirement.This paper proposes a decentralized MADRL method using the maximum reciprocal reward to learn cooperative tracking policies for UAV swarms.This method reshapes each UAV’s reward with a regularization term that is defined as the dot product of the reward vector of all neighbor UAVs and the corresponding dependency vector between the UAV and the neighbors.And the dependence between UAVs can be directly captured by the Pointwise Mutual Information(PMI)neural network without complicated aggregation statistics.Then,the experience sharing Reciprocal Reward Multi-Agent Actor-Critic(MAAC-R)algorithm is proposed to learn the cooperative sharing policy for all homogeneous UAVs.Experiments demonstrate that the proposed algorithm can improve the UAVs’cooperation more effectively than the baseline algorithms,and can stimulate a rich form of cooperative tracking behaviors of UAV swarms.Besides,the learned policy can better scale to other scenarios with more UAVs and targets.