A lot of the research in the field of reinforcement learning has been focused/tested on simulation domains. Some of the characteristics of real-life domains, such as the shape of their noise function or imperfect perception that results in violation of Markovianness are typically neglected in the simulation. In this video, we promote performing reinforcement learning research on real problems to make sure algorithms are more robust to these design imperfections.