Client-Side Network Delay Compensation for Online Shooting Games

Abstract

In online multiplayer shooting games, a long network delay can adversely impact player performance because it leads to large synchronization errors in the game information, such as location errors, between players. To mitigate synchronization errors between players, we propose a novel clientside delay compensation system for online multiplayer shooting games. The proposed delay compensation system comprises regression-based and reinforcement-based methods, which accurately predict present game information based on past game information from within each player's device. Specifically, the regression-based method utilizes linear regression to accurately predict a player's present location based on past location information, whereas the reinforcement-based method utilizes deep reinforcement learning to predict a player's present commands corresponding to the player's current location, based on past snapshots of the game fields. The advantage of the proposed system over the existing server-side delay compensation methods, e.g., Lag Compensation, is that they display accurate game information in each player's device as if the player devices were synchronized with each other; thus, any unfair advantages to the players are prevented. We developed a shooting game simulator to experimentally compare the proposed system with existing ones under constant and fluctuating network delays in loss-free and loss-prone networks. The movement models of different players were considered in the experiments. The results demonstrate that the proposed system simultaneously achieves a lower synchronization error between players and higher shooting accuracy, compared to Lag Compensation. For example, the average decrease in player shooting hit rate under a constant network delay of 100 ms is 0.55, 0.076, and 0.0039 without delay compensation and with the proposed regression-based and reinforcement-based methods, respectively. Under the same network delay, the proposed regression-based and reinforcement-based methods show the improvement of 16 pixels and 14 pixels in terms of the synchronization error compared to Lag Compensation, respectively.