# Noam Brown: from Open AI on solving Poker and Diplomacy with AI

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Text version: https://stenobird.com/podcast/robot-brains-podcast/noam-brown-from-open-ai-on-solving-poker-and-diplomacy-with-ai.md
Podcast: [The Robot Brains Podcast](https://stenobird.com/podcast/robot-brains-podcast)
Published: 2023-06-28T20:14:45+00:00
Episode link: https://www.therobotbrains.ai/who-is-noam-brown
Audio file: https://sphinx.acast.com/p/open/s/6053a29a0d11b0148adcfc96/e/649c94b6ab8b53001141f804/media.mp3
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JSON: https://stenobird.com/v1/public/podcasts/robot-brains-podcast/episodes/noam-brown-from-open-ai-on-solving-poker-and-diplomacy-with-ai
Duration seconds: 4478

## Resource

Noam Brown explains how AI can master imperfect information games like Poker and Diplomacy through strategic planning and self-play. He explores the transition from solving zero-sum games to navigating complex human-like negotiations.

## Highlights
- Main idea: Games serve as natural, un-overfittable benchmarks for evaluating AI progress because they are easy to score and compare against human experts
- Technical insight: Using entropy regularization helps ensure that subgame equilibria remain consistent with the original game's equilibrium
- Failure mode: The success of reinforcement learning in Chess and Go does not automatically translate to games with hidden information or high variance
- Practical takeaway: Future breakthroughs in AI reasoning may come from integrating planning and strategic anticipation into large language models
- Research insight: Effective AI in Diplomacy requires managing complex, human-like dialogue and negotiating trust among multiple players

## Topics

Artificial Intelligence, Game Theory, Poker AI, Large Language Models, Reinforcement Learning, Diplomacy AI, Strategic Planning, Imperfect Information Games

## Chapters
- 6:40 — The Challenge of Imperfect Information: Comparing the asymmetry of information in poker to the perfect information found in chess and Go.
- 18:35 — Anticipation and Future States: How AI uses planning to anticipate opponent moves and evaluate possible future game states.
- 30:00 — Achieving Equilibrium via Regularization: A technical look at using entropy regularization to solve equilibrium problems in complex subgames.
- 35:30 — Beating Human Professionals: The story behind Libratus and the difficulty of scaling poker AI from heads-up to multi-player formats.
- 52:40 — Limitations of Reinforcement Learning: Why the paradigms used for Chess and Go fail when applied to games with high uncertainty.
- 58:00 — AI in Diplomacy and Dialogue: Using language to facilitate human-like negotiation and strategic communication in complex games.
- 1:04:00 — The Future of LLM Reasoning: Exploring the intersection of large language models and strategic planning capabilities.

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## Transcript

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