- Keywords: Reinforcement Learning, Deep Learning, theory, practice
- Abstract: Recent advances in computation power accessible to machine learning researchers has sparked the flurry of research interest in large scale Reinforcement Learning (RL). However, this interest is facing RL unique characteristics; Any theoretical study as well as empirical investigation of any innovative RL algorithm requires an extensive amount of experts' time and imposes intolerably massive computation cost. These challenges are even more vital in RL due to the significantly limited dedicated resources, such as human-time and computation resources, to RL research. These hardnesses and enormous costs impel an immediate consideration in both guiding our research directions and allocating our resources to advance RL in practice. We devote this paper to discuss a set of essential issues that are necessary to be addressed in order to provide greater supervisions in conducting future RL studies.