But that's just one major hole in what the authors describe as "superhuman" AI demos in games like chess and go. The other is that those games don't look at all like most conventional digital games people enjoy playing.
Go looks like matrix math. A neural program has a chance of learning an internal simulation of the game. There's no chance of that for DOTA 2 or StarCraft II, which is why those wins by reinforcement learning AI bots were so impressive.
However, those games (DOTA 2 and SC2) suffer from big rewards for superhuman dexterity. Just a few seconds of excess dexterity (not strictly superhuman dexterity) can tip the game to an absolute win for an AI bot. So most conventional performance diagnostics, like winrates or Elo scores, greatly understate the role of dexterity in those games.
Hanabi seems like a great reaction to where the games AI research is going. Hanabi will be easier to simulate than a game like Hearthstone, so there will be accelerated implementations or neural programs of it. However, the authors criticize something that might be vital to Hanabi research:
> "However, it is impractical for human Hanabi players to expect others to strictly abide by a preordained policy."
That seems unfair. If you're going to research an idiosyncratically cooperative game, the most competitive strategies are going to involve preordained (meaning coordinated prior to a match) policies. Why turn your back on the path towards strategy development that obviously leads to the best play?
The biggest problem with Hanabi is that, as a cooperative game, we have no real idea what great strategies for it are. I know the authors claim that they do. But for DOTA 2 and StarCraft 2, PvP games with immense popularity, we have a credible natural laboratory for strategy and skill development. Hanabi is really obscure by comparison.
The biggest problems with Hearthstone, which in my opinion is one of the best games to research, are that (1) the rules are way too complex for a neural program to learn them, so you need novel approaches for learning, (2) the rules are too complex to execute on a GPU, so you need novel approaches to parallelized computing and optimization, and (3) the deckbuilding meta and some pay-to-win design have stunted strategy development, so outperforming a human opponent may be way too easy.
There are so many problems to solve, and it's not that credible that neural networks are the path forward!