The four humans were getting $120,000 between them. Their share of that was dependent on how much better they did than the other humans. That means there was no incentive to collude.
Top pro poker players understand the value of money. They weren't treating it as a freeroll and anyone that has seen the hand histories can confirm that.
That's not true in practice for poker. Pluribus showed that if you run CFR in multiplayer poker you get a solution that works great in practice. Multiple equilibria are certainly a theoretical issue for many games, but poker conveniently isn't one of them.
The remaining challenge is getting it to play well with human partners. Doing that requires modeling human conventions rather than learning weird bot conventions. That's hard because while you can collect essentially unlimited data through self play, it's hard to collect a lot of data playing with humans using reinforcement learning. AI algorithms are really bad at sample efficiency.
Normally 10,000 hands would be too small a sample size but we used variance-reduction techniques to reduce the luck factor. Think things like all-in EV but much more powerful. It's described in the paper.
Bridge has a similar challenge, though from what I understand Bridge AIs are not superhuman yet. I suspect our techniques could be applied to Bridge, though they may need to be adapted a bit.
The imperfect information in Hanabi absolutely matters a ton. It's not an interesting game without it.
Thanks! We're looking in a few different directions, but one thing I'm excited about is mixed cooperative/competitive settings. In poker, there is no room for cooperation. In Hanabi, you are 100% cooperating with your teammates. But most real-world situations, like a negotiation, are somewhere in between. The AI techniques for these settings are not too strong yet.
In terms of Hanabi, this bot arrived at conventions that are pretty different from how humans play the game. We invited an advanced Hanabi player to play with the bot and he pointed out a few things in particular that he'd like to start using. For example, humans usually have a rule that if your teammate hints multiple cards of the same color/number, you should play the newest one. The bot uses a more complicated rule: if the card you just picked up was hinted then play that card, otherwise play the oldest hinted card. That gives you way more flexibility to hint playable cards that would otherwise be tough to get played.
I think one important general lesson is that search is really, really important. Deep RL algorithms are making huge advancements, but Deep RL alone can't reach superhuman performance in Go or poker with search. Here, too, we see that search was the key to conquering this game, and I think that will hold true in more complex real-world settings as well. Figuring out how to extend search to more complex real-world settings will be a challenge, but it's one worth pursuing.
The search algorithm shares a lot in common with our Pluribus poker AI (https://ai.facebook.com/blog/pluribus-first-ai-to-beat-pros-...), but we added "retrospective belief updates" which makes it way more scalable. We also didn't use counterfactual regret minimization (CFR) because in cooperative games you want to be as predictable as possible, whereas CFR helps make you unpredictable in a balanced way (useful in poker).
The most surprising takeaway is just how effective search was. People were viewing Hanabi as a reinforcement learning challenge, but we showed that adding even a simple search algorithm can lead to larger gains than any existing deep RL algorithm could achieve. Of course, search and RL are completely compatible, so you can combine them to get the best of both worlds, but I think a lot of researchers underestimated the value of search.
There was real money at stake in this experiment. The pros were guaranteed $0.40 per hand just for participating, but that could increase to $1.60 per hand depending on how well they did.
To answer your question, no, I don't think human players would play at their best when not playing for actual money.
We played 10,000 hands of poker in the 5 humans + 1 AI experiment. The number of hands won isn't a useful metric in poker. If you win only 10% of your hands and make $1,000 on those hands, while losing only $1 on the other 90% of hands, then you're a winning player. The bot won at a rate of 4.8 bb/100 ($4.8 per hand if the blinds are $50/$100). This is considered a large win rate by professionals.
Our goal is to make the research as accessible as possible to the AI community, so we include descriptions of the algorithms and pseudocode in the supplementary material. However, in part due to the potential negative impact this code could have on online poker, we're not releasing the code itself.