使用gRPC提高DareFightingICE中AI的数据传输效率
Improving Data Transfer Efficiency for AIs in the DareFightingICE using gRPC
摘要：
本文提出了一种新的通信接口的DareFightingICE平台，基于Java的格斗游戏的重点是实现AI控制非玩家角色。该接口使用开源远程过程调用gRPC来提高游戏和AI之间的数据传输效率，减少从游戏服务器接收信息所花费的时间。这很重要，因为在格斗游戏中实现AI的主要挑战是需要AI在短响应时间内选择要执行的动作。DareFightingICE平台已经与Py 4J集成，允许开发人员使用Python创建AI。然而，Py 4J在处理大量数据时效率较低，导致延迟过大。相比之下，gRPC非常适合传输大量数据。为了评估新通信接口的有效性，我们进行了一项实验，比较了gRPC和Py 4J的延迟，使用基于规则的AI，无论从游戏服务器接收到的信息如何，都会发送kick命令。实验结果表明，与现有接口相比，该接口不仅延迟降低了65%，而且稳定性得到了提高，并消除了漏帧现象。
英文摘要：
This paper presents a new communication interface for the DareFightingICE platform, a Java-based fighting game focused on implementing AI for controlling a non-player character. The interface uses an open-source remote procedure call, gRPC to improve the efficiency of data transfer between the game and the AI, reducing the time spent on receiving information from the game server. This is important because the main challenge of implementing AI in a fighting game is the need for the AI to select an action to perform within a short response time. The DareFightingICE platform has been integrated with Py4J, allowing developers to create AIs using Python. However, Py4J is less efficient at handling large amounts of data, resulting in excessive latency. In contrast, gRPC is well-suited for transmitting large amounts of data. To evaluate the effectiveness of the new communication interface, we conducted an experiment comparing the latency of gRPC and Py4J, using a rule-based AI that sends a kick command regardless of the information received from the game server. The experiment results showed not only a 65\% reduction in latency but also improved stability and eliminated missed frames compared to the current interface.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
