 Dear Fellow Scholars, this is two-minute papers with Karo Zsolnai-Fehir. This footage that you see here came freshly from Google DeepMind's lab and is about benchmarking reinforcement learning algorithms. Here, you see the classical cardboard swing-up task from this package. As the algorithm starts to play, a score is recorded that indicates how well it is doing and the learner has to choose the appropriate actions depending on the state of the environment to maximize this score. Reinforcement learning is an established research subfield within machine learning with hundreds of papers appearing every year. However, we see that most of them cherry pick a few problems and test against previous works on this very particular selection of tasks. This paper describes a package that is not about the algorithm itself but about helping future research projects to be able to test their results against previous works on an equal footing. This is a great idea which has been addressed earlier by OpenAI with their learning environment by the name Jim. So the first question is, why do we need a new one? The DeepMind Control Suite provides a few differentiating features. One, Jim contains both discrete and continuous tasks where this one is concentrated on continuous problems only. This means that state, time, and action are all continuous which is usually the hallmark of more challenging and lifelike problems. For an algorithm to do well, it has to be able to learn the concept of velocity, acceleration, and other meaningful physical concepts and understand their evolution over time. Two, there are domains where the new control suite is a superset of Jim meaning that it offers equivalent tasks and then some more. And three, the action and reward structures are standardized. This means that the results and learning curves are much more informative and easier to read. This is crucial because research scientists read hundreds of papers every year and this means that they don't necessarily have to look at videos. They immediately have an intuition of how an algorithm works and how it relates to previous techniques just by looking at the learning curve plots. Many tasks also include a much more challenging variant with more sparse rewards. We discussed these sparse rewards in a bit more detail in the previous episode. If you are interested, make sure to click the card on the lower right at the end of this video. The paper also contains an exciting roadmap for future development including quadruped locomotion, multithreaded dynamics, and more. Of course, the whole suite is available free of charge for everyone. The link is available in the description. Super excited to see a deluge of upcoming AI papers and see how they beat the living hell out of each other in 2018. Thanks for watching and for your generous support and I'll see you next time.