# Pluribus Poker

Pluribus is a 2019 AI poker bot developed by Facebook's AI Lab and Carnegie Mellon University, the first to beat top professionals in six-player no-limit Texas hold'em, winning over 30 milli big blinds per game.

Pluribus is a computer poker player using artificial intelligence built by Facebook's AI Lab and Carnegie Mellon University. It plays the poker variation no-limit Texas hold 'em and is the first bot to beat humans in a complex multiplayer competition. The developers published their results in 2019, marking a significant milestone in the field of [artificial intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) and game-playing systems.

According to its creators, developing a superhuman AI for multiplayer poker was the widely recognized main remaining milestone in computer poker prior to Pluribus. The system relies on offline self-play to build a base strategy, but then continues to learn in real-time during its online play. The base strategy was computed in eight days, and at market rates would cost about $144 to produce, much smaller than contemporary superhuman game-playing milestones such as AlphaZero.

## Technical Approach

In AI, two-player zero-sum games (such as heads-up hold'em) are usually won by approximating a Nash equilibrium strategy; however, this approach does not work for games with three or more players. Pluribus instead uses an approach which lacks strong theoretical guarantees, but nevertheless appears to work well empirically at defeating human players. The system's design draws on techniques from [machine learning](https://www.wikiprompt.org/wiki/machine-learning) and [deep learning](https://www.wikiprompt.org/wiki/deep-learning), though it does not rely on a [neural network](https://www.wikiprompt.org/wiki/neural-network) in the same way as many contemporary AI systems. Its real-time learning capability allows it to adapt to opponents' tendencies during play, a feature that distinguishes it from earlier poker bots.

## Performance and Results

Across the competitions, Pluribus won an average of over 30 milli big blinds per game. Playing no-limit hold'em against five professional poker players, Pluribus won an average of $5 per hand with winnings of $1,000 per hour, which Facebook described as a decisive margin of victory. The bot's self-learned play style avoids limping (calling the big blind), and engages in donk betting (ending a round with a call and starting the next round by betting) more often than human experts do. These unconventional strategies proved effective against top-level opposition.

## Reactions from Experts

Among expert poker players, Jason Les stated he felt very hopeless, saying, "You don't feel like there's anything you can do to win." Chris Ferguson stated, "Pluribus is a very hard opponent to play against. It's really hard to pin him down on any kind of hand." Jimmy Chou noted, "Whenever playing the bot, I feel like I pick up something new to incorporate into my game." In The Wall Street Journal, science editor Daniela Hernandez characterized Pluribus as advanced at a key human skill - deception.

## Broader Context and Legacy

The development of Pluribus built on earlier work in computer poker, including heads-up bots that had previously achieved superhuman performance in two-player games. The success in multiplayer no-limit hold'em was considered a major breakthrough because the game involves imperfect information, bluffing, and complex strategic interactions among multiple players. The approach used by Pluribus, which combines offline self-play with real-time adaptation, has influenced subsequent research in game-playing AI and multi-agent systems.

## Source Code and Ethical Considerations

Following the victory, the developers declined to release the source code, out of fear it would be misused to surreptitiously cheat against human poker players in online matches. This decision highlighted growing concerns about the potential misuse of advanced AI systems in competitive and financial contexts. The case of Pluribus has been discussed in the context of AI safety and the responsible deployment of powerful algorithms, alongside other notable systems developed by organizations such as [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind) and [OpenAI](https://www.wikiprompt.org/wiki/openai).

## References

- Facebook AI Lab and Carnegie Mellon University research publications (2019)
- The Wall Street Journal coverage by Daniela Hernandez
- Statements from professional poker players Jason Les, Chris Ferguson, and Jimmy Chou

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Source: https://www.wikiprompt.org/wiki/pluribus-poker
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-07T21:32:32.542037+00:00