Lex Fridman Podcast
#344 – Noam Brown: AI vs Humans in Poker and Games of Strategic Negotiation
06 Dec 2022
Noam Brown is a research scientist at FAIR, Meta AI, co-creator of AI that achieved superhuman level performance in games of No-Limit Texas Hold'em and Diplomacy. Please support this podcast by checking out our sponsors: - True Classic Tees: https://trueclassictees.com/lex and use code LEX to get 25% off - Audible: https://audible.com/lex to get 30-day free trial - InsideTracker: https://insidetracker.com/lex to get 20% off - ExpressVPN: https://expressvpn.com/lexpod to get 3 months free EPISODE LINKS: Noam's Twitter: https://twitter.com/polynoamial Noam's LinkedIn: https://www.linkedin.com/in/noam-brown-8b785b62/ webDiplomacy: https://webdiplomacy.net/ Noam's papers: Superhuman AI for multiplayer poker: https://par.nsf.gov/servlets/purl/10119653 Superhuman AI for heads-up no-limit poker: https://par.nsf.gov/servlets/purl/10077416 Human-level play in the game of Diplomacy: https://www.science.org/doi/10.1126/science.ade9097 PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: - Check out the sponsors above, it's the best way to support this podcast - Support on Patreon: https://www.patreon.com/lexfridman - Twitter: https://twitter.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Medium: https://medium.com/@lexfridman OUTLINE: Here's the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) - Introduction (05:37) - No Limit Texas Hold 'em (09:30) - Solving poker (22:40) - Poker vs Chess (29:18) - AI playing poker (1:02:46) - Heads-up vs Multi-way poker (1:13:37) - Greatest poker player of all time (1:17:10) - Diplomacy game (1:27:01) - AI negotiating with humans (2:09:26) - AI in geopolitics (2:14:11) - Human-like AI for games (2:20:12) - Ethics of AI (2:24:26) - AGI (2:28:25) - Advice to beginners
Chapter 1: What is the significance of No Limit Texas Hold'em in AI research?
The following is a conversation with Noah Brown, research scientist at FAIR, Facebook AI research group at Meta AI. He co-created the first AI system that achieved superhuman level performance in No Limit Texas Hold'em, both heads up and multiplayer.
And now, recently, he co-created an AI system that can strategically out-negotiate humans using natural language in a popular board game called Diplomacy, which is a war game that emphasizes negotiation. And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast.
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This episode is brought to you by Audible, an audiobook service that has given me hundreds, if not thousands of hours of education through listening to audiobooks. I'm currently re-listening for the, I don't know how many times I've re-listened to this book, how many times I've read it, but George Orwell's 1984. Not nearly as many times as Animal Farm.
I don't know why exactly, but Animal Farm, just as a story, just connects with me. A basic fable. of creatures being not so good to each other and being greedy and also being good to each other and having personalities and just in the simple way that sort of whenever animals are made in a children-like story, it's simple, but it can still get to the profound.
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Chapter 2: How does No Limit Texas Hold'em differ from Limit Hold'em?
It's not every hand is a new hand. Is there a continuation in terms of estimating what kind of player I'm facing here?
That's a good question. So you could approach the game that way. The way that the bots do it, and the way that humans approach it also, expert human players, the way they approach it is to basically assume that you know my strategy. So...
I'm going to try to pick a strategy where even if I were to play it for 10,000 hands and you could figure out exactly what it was, you still wouldn't be able to beat it. Basically, what that means is I'm trying to approximate the Nash equilibrium.
I'm trying to be perfectly balanced because if I'm playing the Nash equilibrium, even if you know what my strategy is, like I said, I'm still unbeatable in expectation. So that's what the bot aims for. And that's actually what a lot of expert poker players aim for as well, to start by playing the Nash equilibrium.
And then maybe if they spot weaknesses in the way you're playing, then they can deviate a little bit to take advantage of that.
They aim to be unbeatable in expectation. Okay, so who's the greatest poker player of all time and why is it Phil Hellmuth? So this is for Phil. So he's known, at least in part, for maybe playing suboptimally and he still wins a lot. It's a bit chaotic. So maybe can you speak from an AI perspective about the genius of his madness or the madness of his genius?
So playing suboptimally, playing chaotically, as a way to make you hard to pin down about what your strategy is.
So, okay. The thing that I should explain, first of all, with like Nash equilibrium, it doesn't mean that it's predictable. The whole point of it is that you're trying to be unpredictable.
Now, I think when somebody like Phil Helmuth might be really successful is not in being unpredictable, but in being able to take advantage of the other player and figure out where they're being predictable, right? or guiding the other player into thinking that you have certain weaknesses and then understanding how they're going to change their behavior.
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Chapter 3: What strategies are employed in No Limit Texas Hold'em?
What's that look like? There's different actions like raising, calling. Yeah, what are the actions... Is it just a search over actions?
So in a game like chess, the search is like, okay, I'm in this chess position and I can move these different pieces and see where things end up. In poker, what you're searching over is the actions that you can take for your hand, the probabilities that you take those actions, and then also the probabilities that you take other actions with other hands that you might have.
And that's kind of hard to wrap your head around. Why are you searching over these other hands that you might have and trying to figure out what you would do with those hands? And the idea is, again, you want to always be balanced and unpredictable. If your search algorithm is saying, oh, I want to raise with this hand, well, in order to know whether that's a good action, let's say it's a bluff.
Let's say you have a bad hand and you're saying, oh, I think I should be betting here with this really bad hand and bluffing. Well, that's only a good action if you're also betting with a strong hand. Otherwise, it's an obvious bluff.
So if your action in some sense maximizes your unpredictability, so that action could be mapped by your opponent to a lot of different hands, then that's a good action.
Basically, what you want to do is put your opponent into a tough spot. So you want them to always have some doubt, like, should I call here? Should I fold here? And if you are raising in the appropriate balance between bluffs and good hands, then you're putting them into that tough spot. And so that's what we're trying to do.
We're always trying to search for a strategy that would put the opponent into a difficult position.
can you give a metric that you're trying to maximize or minimize? Does this have to do with the regret thing that we're talking about in terms of putting your opponent in a maximally tough spot?
Yeah, ultimately what you're trying to maximize is your expected winnings, like your expected value, the amount of money that you're going to walk away from, assuming that your opponent was playing optimally in response.
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Chapter 4: What are the implications of Nash Equilibrium in poker?
He was excited.
He was excited. And he honestly, he wanted to play against the bot. He thought he had a decent chance of beating it. I think, you know, this was like several years ago when I think it was like not as clear to everybody that, you know, the AIs were taking over. I think now people recognize that like if you're playing against a bot, there's like no chance that you have in a game like poker.
So consistently the bots will win. The bots have heads up and in other variants too. So multi six player Texas Hold'em, no limit Texas Hold'em is the bots win.
Yeah, that's the case. So I think there's some debate about like, is it true for every single variant of poker? I think for every single variant of poker, if somebody really put in the effort, they can make an AI that would beat all humans at it. We've focused on the most popular variants. So heads up, no limit, Texas Hold'em.
And then we followed that up with six-player poker as well, where we managed to make a bot that beat expert human players. And I think even there now, it's pretty clear that humans don't stand a chance.
See, I would love to hook up an AI system that looks at EEG, like how, like actually tries to optimize the toughness of the spot it puts a human in. And I would love to see how different is that from the game theory optimal. So you try to maximize the heart rate of the human player.
Yeah.
Like the freaking out over a long period of time. I wonder if there's going to be different strategies that emerge that are close in terms of effectiveness. Because something tells me you could still achieve superhuman level performance by just making people sweat.
I feel like there's a good chance that that is the case, yeah. It's like a decent proxy for score, right? And this is actually the common poker wisdom when they're teaching players. Before there were bots and they were trying to teach people how to play poker, they would say the key to the game is to put your opponent to difficult spots.
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Chapter 5: What are the unique challenges of AI in poker compared to other games?
The problem in Go was that, or the final problem in Go at least, was that nobody had a good way of looking at a board and figuring out who was winning or losing, and describing through a simple algorithm who was winning or losing.
And so there neural nets were super helpful because you could just feed in a ton of different board positions into this neural net and it would be able to predict then who was winning or losing. But in poker, the features weren't the challenge.
The challenge was how do you design a scalable algorithm that would allow you to find this balanced strategy that would understand that you have to bluff with the right probability.
So can that be somehow incorporated into the value function, the complexity of poker that you've described?
Yeah, so the way the value functions work in poker, like the latest and greatest poker AIs, they do use neural nets for the value function. The way it's done is very different from how it's done in a game like chess or go, because in poker, you have to reason about beliefs. And so the value of a state depends on the beliefs that players have about what the different cards are.
Like if you have pocket aces, then whether that's a really, really good hand or just an okay hand depends on whether you know I have pocket aces. If you know that I have pocket aces, then if I bet, you're going to fold immediately. But if you think that I have a really bad hand, then I could bet with pocket aces and make a ton of money.
So the value function in poker these days takes the beliefs as an input, which is very different from how chess and go AIs work.
So as a person who appreciates the game, who do you think is the greatest poker player of all time?
That's a tough question.
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Chapter 6: Who is considered the greatest poker player of all time?
So the AHS engines can give estimates of the quality of play, right? Yeah. I wonder if there's a, is there an ELO rating type of system for poker? I suppose you could, but there's just not enough. You would have to play a lot of games, right? A very large number of games, like more than you would in chess. The deterministic game makes it easier to estimate ELO.
I think it is much harder to estimate something like ELO rating in poker. I think it's doable. The problem is that the game is very high variance. You could be profitable in poker for a year and you could actually be a bad player just because the variance is so high. You've got top professional poker players that would lose for a year just because they're on a really bad streak.
yeah so for elo you have to have a nice clean way of saying if player a played player b and a beats b that says something that's a signal in poker that's a very noisy signal it's a very noisy signal now there is a signal there and so you could do this this calculation it would just be much harder
Chapter 7: What is the game of Diplomacy and how does it differ from poker?
But the same way that AIs have now taken over chess and all the top professional chess players train with AIs, the same is true for poker. The game has become a very computational. People train with AIs to try to find out where they're making mistakes, try to learn from the AIs to improve their strategy.
Chapter 8: How can AI improve negotiation strategies in games?
So the game has been revolutionized in the past five years by the development of AI in this sport.
The skill with which you avoided the question of the greatest of all time was impressive.
So my feeling is that it's a difficult question because just like in chess, where you can't really compare Magnus Carlsen today to Garry Kasparov because the game has evolved so much. The poker players today are so far beyond the skills of people that were playing even 10 or 20 years ago.
So you look at the kinds of all-stars that were on ESPN at the height of the poker boom, pretty much all those players are actually not that good at the game today. At least the strategy aspect. I mean, they might still be good at reading the player at the other side of the table and trying to figure out, are they bluffing or not?
But in terms of the actual computational strategy of the game, a lot of them have really struggled to keep up with that development. Now, so for that reason, I'll give an answer and I'm going to say Daniel Negreanu, who you actually had on the podcast recently. I saw it was a great episode.
so much and Phil's gonna hate this so much and I'm gonna give him I'm gonna give him credit because he is one of the few like old school really strong players that have kept up with the development of AI so he is trying to he's constantly studying the game theory optimal way of playing exactly yeah and I think a lot of a lot of the old school poker players have just kind of given up on that aspect and I gotta give Daniel Negreanu credit for keeping up with all the developments that are happening in the sport
Yeah, it's fascinating to watch. It's fascinating to watch where it's headed. Yeah, so there you go. Some love for Daniel. Quick pause. Bath and break?
Yeah, let's do it.
Let's go from poker to diplomacy. What is, at a high level, the game of diplomacy?
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