 Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér. We have talked about some awesome previous works where we use learning algorithms to teach digital creatures to navigate in complex environments. The input is a terrain and a set of joints, feet and movement types, and the output has to be a series of motions that maximizes some kind of reward. This previous technique borrowed smaller snippets of movements, from a previously existing database of motions and learned to stitch them together in a way that looks natural. And as you can see, these results are phenomenal. And a selling point of this new one, which you might say looks less elaborate. However, it synthesizes them from scratch. The problem is typically solved via reinforcement learning, which is a technique that comes up with a series of decisions to maximize a prescribed score. This score typically needs to be something reasonably complex. Otherwise, the algorithm is given too much freedom to maximize it. For instance, we may want to teach a digital character to run or jump hurdles, but it may start crawling instead, which is still completely fine if our objective is too simple. For instance, just maximizing the distance from the starting point. To alleviate this, we typically resort to reward engineering, which means that we add additional terms to this reward function to regularize the behavior of these creatures. For instance, we can specify that throughout these motions, the body has to remain upright, which likely favors locomotion type solutions. However, one of the main advantages of machine learning is that we can reuse our solutions for a large set of problems. If we have to specialize our algorithm for all terrain and motion types and different kinds of games, we lose out on the biggest advantage of learning techniques. So researchers at DeepMind decided that they are going to solve this problem with a reward function, which is nothing else but forward progress. That's it. The further we get, the higher score we obtain. This is amazing because it doesn't require any specialized reward function, but at the same time, there are a ton of different solutions that get us far in these terrains. And as you see here, beyond bipeds, a bunch of different agent types are supported. The key factors to make this happen is to apply two modifications to the original reinforcement learning algorithm. One makes the learning process more robust and less dependent on what parameters we choose and the other one makes it more scalable, which means that it is able to efficiently deal with larger problems. Furthermore, the training process itself happens on a rich, carefully selected of challenging levels. Make sure to have a look at the paper for details. A byproduct of this kind of problem formulation is, as you can see, that even though this humanoid does its job with a slower body well, but in the meantime, it is flailing its arms like a madman. The reason is likely because there is not much of a difference in the reward between different arm motions. This means that we most likely get through a maze or a height field even when flailing. Therefore, the algorithm doesn't have any reason to favor more natural looking movements for the upper body. It will probably choose a random one, which is highly unlikely to be a natural motion. This creates high quality, albeit amusing results that I am sure some residents of the internet will honor with a sped up remix video with some Benihil music. In summary, no pre-computed motion database, no handcrafting of rewards, and no additional wizardry needed. Everything is learned from scratch with a few small modifications to the reinforcement learning algorithm. Highly remarkable work. If you've enjoyed this episode and would like to help us and support the series, have a look at our Patreon page. Details and cool perks are available in the video description, or just click the letter P at the end of this video. Thanks for watching and for your generous support, and I'll see you next time.