In this paper, I relate an auto-reflexive analysis of my practice of designing and musicking deep reinforcement learning. Based on technical description of the Co-Explorer, a deep reinforcement learning agent designed to support sonic exploration through positive or negative human feedback, I discuss how deep reinforcement learning can be seen as a form of sonic comprovisational agent, which enables musicians to compose a parameter sound space, then to engage in embodied improvisation by guiding the agent through sound space using feedback. I then relate on my own musicking experiments led with the Co-Explorer, which resulted to the creation of the ægo music performance, and build on these to sketch a music representation for deep reinforcement learning, highlighting its original aesthetics, as well as its ontological shifts between performer and agent, and epistemological tensions with engineering-oriented representations. Rather than discrediting the latters, my wish is to create space for practicebased approaches to machine learning in a way that is complementary to engineering-oriented approaches, while contributing to further music representations and discourses on artificial intelligence.