PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model

📄 arXiv: 2401.06781v1 📥 PDF

作者: Chenghao Huang, Yanbo Cao, Yinlong Wen, Tao Zhou, Yanru Zhang

分类: cs.AI, cs.CL

发布日期: 2024-01-04

💡 一句话要点

提出PokerGPT以解决多玩家德州扑克的决策问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 德州扑克 不完全信息博弈 大语言模型 强化学习 决策生成 轻量级模型 人机交互

📋 核心要点

  1. 现有方法如CFR在计算成本和多玩家扩展性上存在显著挑战，限制了其在实际应用中的可行性。
  2. 本文提出PokerGPT，通过轻量级大语言模型处理德州扑克决策，简化了人机交互过程，提升了决策效率。
  3. 实验结果显示PokerGPT在胜率和响应速度上显著优于传统方法，展现出良好的实用性和推广潜力。

📝 摘要（中文）

扑克，特别是德州扑克，一直是研究不完全信息博弈（IIGs）的典型目标。以往的研究如DeepStack和Libratus主要依赖反事实遗憾最小化（CFR）来解决单对局无上限扑克。然而，CFR的计算成本高且难以扩展到多玩家游戏。本文提出PokerGPT，一个基于轻量级大语言模型（LLM）的端到端求解器，能够处理任意数量的玩家并获得高胜率。PokerGPT通过简单的文本信息生成决策建议，便于人机交互。我们将真实游戏记录转化为提示，并利用强化学习人类反馈技术对轻量级预训练LLM进行微调。实验结果表明，PokerGPT在胜率、模型大小、训练时间和响应速度上均优于以往方法，显示出LLM在解决IIGs中的巨大潜力。

🔬 方法详解

问题定义：本文旨在解决多玩家德州扑克中的决策问题，现有方法如CFR由于计算成本高和游戏树规模指数增长，难以应用于多玩家场景。

核心思路：PokerGPT通过轻量级大语言模型，利用简单的文本信息生成决策建议，降低了人机交互的复杂性，并通过强化学习人类反馈技术进行微调。

技术框架：整体架构包括数据采集、提示工程、模型微调和决策生成四个主要模块。首先从真实游戏中获取文本记录，然后进行提示工程以提取有效信息，接着对模型进行微调，最后生成决策建议。

关键创新：PokerGPT的核心创新在于将轻量级大语言模型应用于多玩家德州扑克的决策生成，显著降低了计算复杂度，并提高了模型的响应速度和实用性。

关键设计：在微调过程中，采用了强化学习人类反馈技术，并通过过滤有用信息、选择高胜率玩家行为等提示工程技术，确保模型学习到有效的决策策略。具体的参数设置和损失函数设计在实验中进行了优化。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，PokerGPT在胜率上超过了以往的CFR方法，模型大小和训练时间显著减少，响应速度提升，显示出在多玩家德州扑克中的优越性能。

🎯 应用场景

PokerGPT的研究成果可广泛应用于在线扑克游戏、智能决策系统以及其他需要处理不完全信息的博弈场景。其轻量级设计使得在资源受限的环境中也能高效运行，具有较高的实际价值和推广潜力。

📄 摘要（原文）

Poker, also known as Texas Hold'em, has always been a typical research target within imperfect information games (IIGs). IIGs have long served as a measure of artificial intelligence (AI) development. Representative prior works, such as DeepStack and Libratus heavily rely on counterfactual regret minimization (CFR) to tackle heads-up no-limit Poker. However, it is challenging for subsequent researchers to learn CFR from previous models and apply it to other real-world applications due to the expensive computational cost of CFR iterations. Additionally, CFR is difficult to apply to multi-player games due to the exponential growth of the game tree size. In this work, we introduce PokerGPT, an end-to-end solver for playing Texas Hold'em with arbitrary number of players and gaining high win rates, established on a lightweight large language model (LLM). PokerGPT only requires simple textual information of Poker games for generating decision-making advice, thus guaranteeing the convenient interaction between AI and humans. We mainly transform a set of textual records acquired from real games into prompts, and use them to fine-tune a lightweight pre-trained LLM using reinforcement learning human feedback technique. To improve fine-tuning performance, we conduct prompt engineering on raw data, including filtering useful information, selecting behaviors of players with high win rates, and further processing them into textual instruction using multiple prompt engineering techniques. Through the experiments, we demonstrate that PokerGPT outperforms previous approaches in terms of win rate, model size, training time, and response speed, indicating the great potential of LLMs in solving IIGs.