 So before wrapping up the segment, let me show you some of the examples of successes in model-free reinforcement learning in a domain that I particularly like, which is robotics. So here's an example from OpenAI of a robot hand that is solving a Rubik's cube. This was done with three RGB cameras, 16 motion capture cameras as sensors, and it was done with 100 years of experience in simulation. What was the equivalent of 100 years of experience in simulation, but using large compute cluster and with accelerated simulations, this amounted to about 50 hours of real-world time. And this was considered as one of the sort of major advances in dexterous manipulation for robots. Things like this are notoriously hard to do with robots. Here's another example. This time in the manipulation setting again, and what it's trying to do is this robot is trying to turn this wheel to some given configuration. And you can see that it's managing to do it. It did it from image observations in this case, a single RGB camera in about 20 hours of experience. So these things do take a really long time to train. More recently from what are called state observations, meaning you're only operating in the space of joint angles, etc. You can teach a walking locomoting robot like these. You can teach it to walk in about two hours from those state observations. So they're pretty low dimensional observations and that's why you can get away with two hours.