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Most autonomous driving studies incorporating vehicle-to-everything (V2X) communications assume the existence of continuous V2X communications, and ignore the possibility that V2X communications can be interrupted intermittently. However, when V2X communications are interrupted, autonomous vehicles that rely on the information from V2X communications can fall into disasters. To address these issues, we propose a novel reinforcement learning method named RL4V2X for decision-making and motion-controlling of autonomous driving. This method is composed of a convolutional neural network (CNN), a gate recurrent unit (GRU) and three gate networks. Specifically, the CNN extracts a vehicle spatial distribution to better represent the traffic. The GRU constructs the sequence features to characterize the dynamics of traffic, which can be used to compensate for the lack of information when V2X communications are interrupted. In addition, the gate networks assign the confidence to different features according to the interruption situations. Extensive simulation results in different traffic conditions demonstrate the superior performance of the proposed method.

The bias problems in recommender systems are an important challenge. In this paper, we focus on solving the bias problems via uniform data. Previous works have shown that simple modeling with a uniform data can alleviate the bias problems and improve the performance. However, the uniform data is usually few and expensive to collect in a real product. In order to use the valuable uniform data more effectively, we propose a novel and general knowledge distillation framework for counterfactual recommendation with four specific methods, including label-based distillation, feature-based distillation, sample-based distillation and model structure-based distillation. Moreover, we discuss the relation between the proposed framework and the previous works. We then conduct extensive experiments on both public and product datasets to verify the effectiveness of the proposed four methods. In addition, we explore and analyze the performance trends of the proposed methods on some key factors, and the changes in the distribution of the recommendation lists. Finally, we emphasize that counterfactual modeling with uniform data is a rich research area, and list some interesting and promising research topics worthy of further exploration. Note that the source codes are available at https://github.com/dgliu/TKDE_KDCRec.