DEGREE PROJECT IN TECHNOLOGY, FIRST CYCLE, 15 CREDITS STOCKHOLM, SWEDEN 2021 Improving Input Prediction in Online Fighting Games Anton Ehlert KTH ROYAL INSTITUTE OF TECHNOLOGY ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Authors Anton Ehlert <
[email protected]> Electrical Engineering and Computer Science KTH Royal Institute of Technology Place for Project Stockholm, Sweden Examiner Fredrik Lundevall KTH Royal Institute of Technology Supervisor Fadil Galjic KTH Royal Institute of Technology Abstract Many online fighting games use rollback netcode in order to compensate for network delay. Rollback netcode allows players to experience the game as having reduced delay. A drawback of this is that players will sometimes see the game quickly ”jump” to a different state to adjust for the the remote player’s actions. Rollback netcode implementations require a method for predicting the remote player’s next button inputs. Current implementations use a naive repeatlast frame policy for such prediction. There is a possibility that alternative methods may lead to improved user experience. This project examines the problem of improving input prediction in fighting games. It details the development of a new prediction model based on recurrent neural networks. The model was trained and evaluated using a dataset of several thousand recorded player input sequences. The results show that the new model slightly outperforms the naive method in prediction accuracy, with the difference being greater for longer predictions. However, it has far higher requirements both in terms of memory and computation cost. It seems unlikely that the model would significantly improve on current rollback netcode implementations. However, there may be ways to improve predictions further, and the effects on user experience remains unknown.