论文标题
在DareFightingIce中学习盲人AI的深入增强
A Deep Reinforcement Learning Blind AI in DareFightingICE
论文作者
论文摘要
本文提出了一个深厚的加固学习剂（AI），它使用声音作为IEEE COG 2022的Darefightingings竞赛的Darefightingings平台上的输入。在这项工作中，仅使用声音的AI，因为输入称为盲人AI。虽然最新的AI主要依赖于其环境提供的视觉或结构化观察结果，但学会从Sound玩游戏仍然是新的，因此具有挑战性。我们提出了不同的方法来处理音频数据，并为盲人AI使用近端策略优化算法。我们还建议利用盲人AI评估提交竞争的声音设计，并为此任务定义两个指标。实验结果不仅表明了我们的盲人AI的有效性，还表明了提出的两个指标的有效性。
This paper presents a deep reinforcement learning agent (AI) that uses sound as the input on the DareFightingICE platform at the DareFightingICE Competition in IEEE CoG 2022. In this work, an AI that only uses sound as the input is called blind AI. While state-of-the-art AIs rely mostly on visual or structured observations provided by their environments, learning to play games from only sound is still new and thus challenging. We propose different approaches to process audio data and use the Proximal Policy Optimization algorithm for our blind AI. We also propose to use our blind AI in evaluation of sound designs submitted to the competition and define two metrics for this task. The experimental results show the effectiveness of not only our blind AI but also the proposed two metrics.
