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Reinforcement learning (RL) research focuses on general solutions that can beapplied across different domains. This results in methods that RL practitionerscan use in almost any domain. However, recent studies often lack theengineering steps ("tricks") which may be needed to effectively use RL, such asreward shaping, curriculum learning, and splitting a large task into smallerchunks. Such tricks are common, if not necessary, to achieve state-of-the-artresults and win RL competitions. To ease the engineering efforts, we distilldescriptions of tricks from state-of-the-art results and study how well thesetricks can improve a standard deep Q-learning agent. The long-term goal of thiswork is to enable combining proven RL methods with domain-specific tricks byproviding a unified software framework and accompanying insights in multipledomains.

Distilling Reinforcement Learning Tricks for Video Games