 Dear Fellow Scholars, this is two-minute papers with Karo Ejolai Fehir. Reinforcement learning is a technique in the field of machine learning to learn how to navigate an elaborate, play a video game, or to teach a digital creature to walk. Usually, we are interested in a series of actions that are in some sense optimal in a given environment. Despite the fact that many enormous tombs exist to discuss the mathematical details, the intuition behind the algorithm itself is incredibly simple. Choose an action, and if you get rewarded for it, keep doing it. If the rewards are not coming, try something else. The reward can be, for instance, our score in a computer game, or how far our digital creature could walk. It is usually quite difficult to learn things where the reward comes long after our action, because we don't know when exactly the point was when we did something well. This is one of the reasons why Google DeepMind will try to conquer strategy games in the future, because this is a genre where good plays usually include long-term planning that reinforcement learning techniques don't really excel at. By the way, this just in, they have just published an excellent paper on including curiosity in this equation in a way that helps long-term planning remarkably. As more techniques pop up in this direction, it is getting abundantly clear that we need a framework where they can undergo stringent testing. This means that the amount of collected rewards and scores should be computed the same way and in the same physical framework. OpenAI is a non-profit company boasting an impressive roster of top-tier researchers who embarked on the quest to develop open and ethical artificial intelligence techniques. We've had a previous episode on this when the company was freshly founded, and as you might have guessed, the link is available in the description box. They have recently published their first major project that goes by the name Jim. Jim is a unified framework that puts reinforcement learning techniques on an equal footing. Anyone can submit their solutions which are run on the same problems, and as a nice bit of gamification, leaderboards are established to see which technique emerges victorious. These environments range from a variety of computer games to different balancing tasks. Some simpler reference solutions are also provided for many of them as a starting point. This place is like Disney World for someone who is excited about the field of reinforcement learning. With more and more techniques, this subfield gets more saturated. It gets more and more difficult to be the first at something. That's a great challenge for researchers. From a consumer point of view, this means that better techniques will pop up day by day. And, as I like to say quite often, we have really exciting times ahead of us. A quick shout out to Experiment, a startup to help research projects come to fruition by crowdsourcing them. Current experiments include really cool projects like how we could implement better anti-doping policies for professional sports, or how to show on a computer screen how our visual imagination works. Thanks for watching and for your generous support, and I'll see you next time.