In light of the rapid emergence of deep reinforcement learning (DRL) in the field of recommender systems, this paper aims to provide a comprehensive overview of recent trends in this area. We begin by highlighting the motivation behind applying DRL in recommender systems, followed by a samples of current DRL-based approaches and existing methods. We also discuss emerging topics, open issues, and provide our perspective on advancing the domain. This survey serves as introductory material for researchers from academia and industry interested in DRL-based recommender systems, while identifying noteworthy opportunities for further research in this exciting field.