  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.
