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The performance of DeepStack and its opponents was evaluated using AIVAT , a provably unbiased low-variance technique based on carefully constructed control variates.
Despite using ideas from abstraction, DeepStack is fundamentally different from abstraction-based approaches, which compute and store a strategy prior to play.
While DeepStack restricts the number of actions in its lookahead trees, it has no need for explicit abstraction as each re-solve starts from the actual public state, meaning DeepStack always perfectly understands the current situation.
Of the 44, poker games played by dozens of players from 17 countries, DeepStack consistently outperformed its human competitors.
This is indeed a question worth asking since most of the literature points in the other direction. Poker bots learn from what human players are doing and adjust their gameplay accordingly.
However, professional poker players have learned many lessons from poker bots. This is particularly true with respect to plays that human players simply don't make regularly.
The scientists behind the creation of Pluribus also created another poker prodigy in the form of Libratus. This bot decisively took down 4 poker professionals over the course of , hands of NLH in a 2-player version of the game.
In the case of Pluribus, the constructs work with proven strategies that allow it to outplay its opponents time and again. By intentionally being unpredictable, poker bots engage in obfuscation techniques which humans can learn from.
Programmers discovered that the algorithm requires 5 continuation strategies for each player to develop an overall strategy for playing them.
By determining how it acts on every hand, given the strength of its hand at any given time, strategies are developed for all possibilities. Humans can certainly commit these lessons to memory and employ them with increasing success rates over time.
There is no doubt that machines are devoid of emotion associated with winning and losing, attachments to money, fear of making specific calls, or the excitement that may otherwise cloud one's judgment.
A poker bot looks only at the current state of the game and how it can make the best decisions in order to consistently win.
In terms of learning processes, Libratus required 15 million core hours to fine-tune its strategies with CPU cores. But the upgraded version, Pluribus required just a fraction of that to become the best multi-player No Limit Texas Hold'em Poker playing bot.
Different poker professionals have different opinions about poker bots and AI. One such poker professional — Daniel Negreanu — has taken an optimistic position on AI and the game of poker.
His perspective is that AI gives you a zero-risk opportunity to learn the game of poker. Previously, poker players learned by making mistakes.
Now technological advances in poker software make it easier for players to improve their game through AI. This all comes full circle to what artificial intelligence really is all about.
In a sense, AI is about giving machines and software copious amounts of data and then using that data for problem-solving purposes. Many poker pros are of the opinion that the psychological component of poker makes it difficult for any machine to understand the nuances of deception, chicanery, posturing, body language, and all behavioral and psychological elements of the game.
Even then, machine learning has narrowed the gap and it is possible for AI technology to learn how players act under certain conditions. Patterns repeat themselves, and poker bots can easily identify different types of poker-playing styles.
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