基于卷积神经网络的“斗地主”策略

Deep neural network has made amazing achievements in various foreign games. In recent years, convolutional neural network has gained great attention because of its unique unit structure, and has been frequently used in game AI agents, such as AlphaGo and Cold Flutter Masters. “Fighting the Landlord” is a typical cooperative game based on incomplete information. In this paper, a 7-layer convolutional neural network DDZ-CNN is designed to train the network with nearly 300,000 pieces of data based on the self-gaming of “Fighting the Landlord” based on Monte Carlo tree to learn the “Fighting the Landlord” strategy. In the training process, the training data are down sampled by a weight-based method to overcome the problem of uneven distribution, and the network can converge quickly. Finally, the trained model is combated with intelligent MCTS model and real person, and a good winning rate is obtained, which verifies the effectiveness and feasibility of the algorithm in this paper.     

LU-Re Net植物叶片分割与计数模型

为完成一种植物叶片分割与计数模型LU-Re Net,利用Xception module和Coord Conv改进模型编码器,通过改进的Re Net模型并参照Unet模型结构将CNN和RNN结合在一起。在CVPPP的A1、A2、A3基准数据集上,LU-Re Net模型取得了0.95的平均F1-score、95.54%的平均前景-背景Dice(FBD%)、0.95的平均召回率和1.91的平均计数差分绝对值(|Dic|)。在A2基准数据集上,该模型表现超越大部分最新算法。实验结果表明,该网络在拥有高分割准确率的同时具有良好的泛化能力。