DotA is substantially different compared to Go in the following main senses:
1) In Go, you are allowed to see the entire board and pieces at all times -- it is a complete information game. On the other hand, games like DotA have partial information because you are not able to see where your opponents are and what they are doing at all times.
2) Go, Chess and many of the Atari games are single player games. OpenAI wanted to see if machine learning can be applied in a multiplayer setting where the problem needs to be solved at a global / team level.
3) DotA has so many mechanics and strategies where you have a lot of choices to be made. One of the challenges is whether one can look at the overall outcome of the game and reason about what particular choices went into the winning or losing of the game. This (long event horizon) makes it extremely difficult to learn such models.
OpenAI interfaced with a complicated game like DotA through an API provided by Valve which made it lot easier. Instead of seeing the game screen, they got snapshots of data of around 35KB per observation (co-ordinates of heroes, creeps etc). In the absence of this API, they would have had to use substantially more computational resources to render the in game graphics and this would also make the training process extremely slow.
This benchmark was an experiment that demonstrated that tackling such a class of problems is indeed possible (given a lot of computational resources and an environment to train in). During the interviews in between and after the games, they mentioned that the algorithms that they have used can have many applications in all fields a̶l̶t̶h̶o̶u̶g̶h̶ ̶s̶p̶e̶c̶i̶f̶i̶c̶ ̶e̶x̶a̶m̶p̶l̶e̶s̶ ̶w̶e̶r̶e̶ ̶n̶o̶t̶ ̶p̶r̶o̶v̶i̶d̶e̶d̶ @crsv's comment describes the example they talked about.
1) In Go, you are allowed to see the entire board and pieces at all times -- it is a complete information game. On the other hand, games like DotA have partial information because you are not able to see where your opponents are and what they are doing at all times.
2) Go, Chess and many of the Atari games are single player games. OpenAI wanted to see if machine learning can be applied in a multiplayer setting where the problem needs to be solved at a global / team level.
3) DotA has so many mechanics and strategies where you have a lot of choices to be made. One of the challenges is whether one can look at the overall outcome of the game and reason about what particular choices went into the winning or losing of the game. This (long event horizon) makes it extremely difficult to learn such models.
OpenAI interfaced with a complicated game like DotA through an API provided by Valve which made it lot easier. Instead of seeing the game screen, they got snapshots of data of around 35KB per observation (co-ordinates of heroes, creeps etc). In the absence of this API, they would have had to use substantially more computational resources to render the in game graphics and this would also make the training process extremely slow.
This benchmark was an experiment that demonstrated that tackling such a class of problems is indeed possible (given a lot of computational resources and an environment to train in). During the interviews in between and after the games, they mentioned that the algorithms that they have used can have many applications in all fields a̶l̶t̶h̶o̶u̶g̶h̶ ̶s̶p̶e̶c̶i̶f̶i̶c̶ ̶e̶x̶a̶m̶p̶l̶e̶s̶ ̶w̶e̶r̶e̶ ̶n̶o̶t̶ ̶p̶r̶o̶v̶i̶d̶e̶d̶ @crsv's comment describes the example they talked about.
Perhaps an ELI15, but hope this helps.