# Achieving Fairness in DareFightingICE Agents Evaluation Through a Delay Mechanism

arXiv: https://arxiv.org/abs/2312.16010 · HTML: https://mlsanity.com/paper/2312.16010/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 26 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [DareFightingICE Competition: A Fighting Game Sound Design and AI Competition](https://mlsanity.com/paper/2203.01556/) (2022)
- [A Deep Reinforcement Learning Blind AI in DareFightingICE](https://mlsanity.com/paper/2205.07444/) (2022)
- [Improving Data Transfer Efficiency for AIs in the DareFightingICE using gRPC](https://mlsanity.com/paper/2303.10001/) (2023)

## Abstract (arXiv)

This paper proposes a delay mechanism to mitigate the impact of latency differences in the gRPC framework--a high-performance, open-source universal remote procedure call (RPC) framework--between different programming languages on the performance of agents in DareFightingICE, a fighting game research platform. The study finds that gRPC latency differences between Java and Python can significantly impact real-time decision-making. Without a delay mechanism, Java-based agents outperform Python-based ones due to lower gRPC latency on the Java platform. However, with the proposed delay mechanism, both Java-based and Python-based agents exhibit similar performance, leading to a fair comparison between agents developed using different programming languages. Thus, this work underscores the crucial importance of considering gRPC latency when developing and evaluating agents in DareFightingICE, and the insights gained could potentially extend to other gRPC-based applications.