A PPO agent with a Transformer encoder beats prompted LLMs and a history-limited Transformer baseline at Da Vinci Code, winning 58.5% of evaluation games.
Mastering the Game of Guandan with Deep Reinforcement Learning and Behavior Regulating
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abstract
Games are a simplified model of reality and often serve as a favored platform for Artificial Intelligence (AI) research. Much of the research is concerned with game-playing agents and their decision making processes. The game of Guandan (literally, "throwing eggs") is a challenging game where even professional human players struggle to make the right decision at times. In this paper we propose a framework named GuanZero for AI agents to master this game using Monte-Carlo methods and deep neural networks. The main contribution of this paper is about regulating agents' behavior through a carefully designed neural network encoding scheme. We then demonstrate the effectiveness of the proposed framework by comparing it with state-of-the-art approaches.
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cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Mastering Da Vinci Code: A Comparative Study of Transformer, LLM, and PPO-based Agents
A PPO agent with a Transformer encoder beats prompted LLMs and a history-limited Transformer baseline at Da Vinci Code, winning 58.5% of evaluation games.