What is the purpose of having deep learning run on games like AlphaGo and DOTA2, instead of having them train on more general or real world tasks? Is it a constraint on the amount of data, since in video games you can easily generate more?
The data generation is indeed one key aspect of it. To train a reinforcement learning model such as this one, you do need an insane amount of data (they wrote somewhere that the model played the equivalent of 180 years of Dota per day).
Overall, games are a good playground to test ideas and verify assumptions. The next step to transfer this type of knowledge to real world problems would be to build a simulator, train on it using ungodly amounts of computing resources, and then fine-tune the final model on the real world thing. This has been done for robot control tasks in the past. But first, you have to develop and prove that the base learning algorithm works -- and games are nice for that.
This here is also a good showcase of collaboration learned by RL agents, and beating pro teams in an esport where prize pools range in the millions of dollars is an amazing way to convince people.
Training RL agents in the real world is expensive and thus not parallelizable. The current focus on games and VR simulations of robots is exactly because of this reason. The RL agents are much more "sample inefficient" than humans, meaning they need more experiences to learn a skill.
And we, humans (and animals) have a huge environment with billions of agents and millions of years of evolution behind us which allows us to come preloaded with good instincts, they are trying to replicate this process in a few months.
