Summary of Pokergpt: An End-to-end Lightweight Solver For Multi-player Texas Hold’em Via Large Language Model, by Chenghao Huang et al.
PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold’em via Large Language Model
by Chenghao Huang, Yanbo Cao, Yinlong Wen, Tao Zhou, Yanru Zhang
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary PokerGPT is an end-to-end solver for playing Texas Hold’em with arbitrary numbers of players, achieving high win rates. Built on a lightweight large language model (LLM), PokerGPT requires only simple textual information about Poker games to generate decision-making advice, enabling convenient human-AI interaction. Previous works like DeepStack and Libratus relied on counterfactual regret minimization (CFR) for heads-up no-limit Poker but were limited by expensive computational costs and difficulty applying CFR to multi-player games. PokerGPT overcomes these limitations by fine-tuning a pre-trained LLM using reinforcement learning human feedback, outperforming previous approaches in win rate, model size, training time, and response speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PokerGPT is a new way for computers to play Texas Hold’em poker with anyone. It’s like having a super smart friend who can give you advice on what cards to play next. This makes it easy for people to interact with the computer and have fun playing together. The old ways of making computers play poker were complicated and slow, but PokerGPT is fast and works well with any number of players. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Reinforcement learning
