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.
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.