Outbidding and Outbluffing Elite Humans: Mastering Liar's Poker via Self-Play and Reinforcement Learning
Published 7 Nov 2025 · arXiv · Richard Dewey
Overview
Solly, an AI agent, has been developed to play Liar's Poker at an elite human level using self-play and reinforcement learning. This achievement highlights AI's potential in mastering complex, multi-player games with imperfect information.
Key Insights
- Elite Performance: Solly achieved a win rate of over 50% in Liar's Poker, indicating its capability to compete with top human players.
- Advanced Strategies: Solly developed novel bidding strategies and effectively randomized play, making it difficult for human players to exploit.
- Comparison with LLMs: Solly outperformed large language models in strategic play, showcasing its superior decision-making abilities.
BFSI Relevance
Why Relevant
AI's ability to master complex games like Liar's Poker demonstrates its potential in financial decision-making environments characterized by uncertainty and strategic interaction.
Primary Sector
Financial Services
Subsectors
Asset Management, Trading
Actionable Implications
- Explore AI applications in strategic financial decision-making.
- Consider AI for enhancing trading strategies and risk management.
- Evaluate AI's role in competitive environments with incomplete information.
researcher peer-reviewed-paper global