Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence (AI) by endowing autonomous systems with high levels of understanding of the real world. Currently, deep learning (DL) is enabling DRL to effectively solve various intractable problems in various fields. Most importantly, DRL algorithms are also being employed in audio signal processing to learn directly from speech, music and other sound signals in order to create audio-based autonomous systems that have many promising application in the real world. In this article, we conduct a comprehensive survey on the progress of DRL in the audio domain by bringing together the research studies across different speech and music-related areas. We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain. We conclude by presenting challenges faced by audio-based DRL agents and highlighting open areas for future research and investigation.

中文翻译：

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基于音频的应用程序的深度强化学习调查

深度强化学习（DRL）通过赋予对现实世界有较高了解的自主系统，有望彻底改变人工智能（AI）领域。当前，深度学习（DL）使DRL能够有效解决各个领域中的各种棘手问题。最重要的是，DRL算法还被用于音频信号处理中，以直接从语音，音乐和其他声音信号中学习，以创建基于音频的自主系统，该系统在现实世界中具有许多有希望的应用。在本文中，我们通过汇总跨不同语音和音乐相关领域的研究成果，对音频领域DRL的进展进行了全面的调查。我们首先介绍DL和强化学习（RL）的一般领域，然后介绍主要的DRL方法及其在音频领域的应用。最后，我们提出了基于音频的DRL代理所面临的挑战，并着重指出了可供将来研究和调查的开放领域。