 Dear Fellow Scholars, this is two-minute papers with Karo Zsolnai-Fehir. Reinforcement learning is a learning algorithm that chooses a set of actions in an environment to maximize the score. This class of techniques enables us to train an AI to master a large variety of video games and has many more cool applications. Reinforcement learning typically works well when the rewards are dense. What does this mean exactly? This means that if we play a game and after making a mistake we immediately die, it is easy to identify which action of ours was the mistake. However, if the rewards are sparse, we are likely playing something that is akin to a long-term strategy planner game. If we lost, it is possible that we were outmaneuvered in the final battle, but it is also possible that we lost the game way earlier due to building the wrong kind of economy. There are a million other possible reasons because we get feedback on how well we have done only once and much, much after we have chosen our actions. Learning from sparse rewards is very challenging, even for humans. And it gets even worse. In this problem formulation, we don't have any teachers that guide the learning of the algorithm and no prior knowledge of the environment. So this problem sounds almost impossible to solve. So what did DeepMind scientists come up with to at least have a chance of approaching it? And now, hold on to your papers because this algorithm learns like a baby learns about its environment. This means that before we start solving problems, the algorithm would be unleashed into the environment to experiment and master basic tasks. In this case, our final goal would be to tidy up the table. First, the algorithm learns to activate its haptic sensors, control the joints and fingers, then it learns to grab an object and then to stack objects on top of each other. And in the end, the robot will learn that tidying up is nothing else but a sequence of these elementary actions that it had already mastered. The algorithm also has an internal scheduler that decides which should be the next action to master while keeping in mind that the goal is to maximize progress on the main task. Which is tidying up the table in this case. And now, on to validation. When we are talking about software projects, the question of real life viability often emerges. So the question is how would this technique work in reality and what else would be the ultimate test than running it on a real robot arm? Let's look here and marvel at the fact that it easily finds and moves the green block to the appropriate spot. And note that it had learned how to do it from scratch much like a baby would learn to perform such tasks. And also note that this was a software project that was deployed on this robot arm, which means that the algorithm generalizes well for different control mechanisms. A property that is highly sought after when talking about intelligence. And if earlier progress in machine learning research is indicative of the future, this may learn how to perform backflips and play video games on a super human level. And I will be here to report on that for you. Thanks for watching and for your generous support and I'll see you next time.