While supervised and unsupervised learning have been extensively used for knowledge discovery for decades and have achieved immense success, much less attention has been paid to reinforcement learning in knowledge discovery until the recent emergence of deep reinforcement learning (DRL). By integrating deep learning into reinforcement learning, DRL is not only capable of continuing sensing and learning to act, but also capturing complex patterns with the power of deep learning. Recent years have witnessed the enormous success of DRL for numerous domains such as the game of Go, video games, and robotics, leading up to increasing advances of DRL for knowledge discovery. For instance, RL-based recommender systems have been developed to produce recommendations that maximize user utility (reward) in the long run for interactive systems; RL-based traffic signal systems have been designed to control traffic lights in real time to enhance traffic efficiency for urban computing. Similar excitement has been generated in other areas of knowledge discovery, such as graph optimization, interactive dialogue systems, and big data systems. While these successes show the promise of DRL, applying learning from game-based DRL to knowledge discovery is fraught with unique challenges, including, but not limited to, extreme data sparsity, power-law distributed samples, and large state and action spaces. Therefore, it is timely and necessary to provide a venue, which can bring together academia researchers and industry practitioners (1) to discuss the principles, limitations and applications of DRL for knowledge discovery; and (2) to foster research on innovative algorithms, novel techniques, and new applications of DRL to knowledge discovery.