Attentive Relation Network for Object based Video Games

Hangyu Deng, Jia Luo, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Deep reinforcement learning algorithms have made great progress in video games. However, there are still some problems, such as sample inefficiency and poor generalization. In this paper, we highlight that these problems are partially caused by the inability of convolutional neural networks (CNNs) to reason with the underlying relations between the objects in the image observations. Based on this point, we try to alleviate these problems in a more efficient and explainable way, including learning the representations of objects and reasoning the relations between them with a relation network (RN). Each pixel in the feature maps is treated as an object and our model explicitly learns the relations between object pairs. The relations are summarized through an attention mechanism and then fed into the downstream fully-connected layers. In the experiments, our model is compared with baseline models in three typical object based Atari games. Under the same hyperparameter settings, our model still achieves better sample efficiency and generalization capability. Further studies throw light on the impact of hyperparameters and verify the interpretability of the model.

Original languageEnglish
Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738133669
Publication statusPublished - 2021 Jul 18
Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: 2021 Jul 182021 Jul 22

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2021 International Joint Conference on Neural Networks, IJCNN 2021
CityVirtual, Shenzhen

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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