  Reinforcement learning (RL) research focuses on general solutions that can be
applied across different domains. This results in methods that RL practitioners
can use in almost any domain. However, recent studies often lack the
engineering steps ("tricks") which may be needed to effectively use RL, such as
reward shaping, curriculum learning, and splitting a large task into smaller
chunks. Such tricks are common, if not necessary, to achieve state-of-the-art
results and win RL competitions. To ease the engineering efforts, we distill
descriptions of tricks from state-of-the-art results and study how well these
tricks can improve a standard deep Q-learning agent. The long-term goal of this
work is to enable combining proven RL methods with domain-specific tricks by
providing a unified software framework and accompanying insights in multiple
domains.
