In the realm of artificial intelligence and card games, this study introduces a two-step reinforcement learning (RL) strategy tailored for "The Lord of the Rings: The Card Game (LOTRCG)," a complex multistage strategy card game. This research diverges from conventional RL methods by adopting a phased learning approach, beginning with a foundational learning stage in a simplified version of the game and subsequently progressing to the complete, intricate game environment. This methodology notably enhances the AI agent's adaptability and performance in the face of LOTRCG's unpredictable and challenging nature. The paper also explores a multi-agent system, where distinct RL agents are employed for various decision-making aspects of the game. This approach has demonstrated a remarkable improvement in game outcomes, with the RL agents achieving a winrate of 78.5% across a set of 10,000 random games.

翻译：在人工智能与卡牌游戏领域，本研究针对《魔戒：卡牌游戏》（LOTRCG）这一复杂多阶段策略卡牌游戏，提出了一种两步强化学习策略。该方法摒弃传统强化学习的单一训练模式，采用分阶段学习框架：首先在简化版游戏环境中建立基础学习阶段，随后过渡至完整复杂的游戏环境。该策略显著提升了AI代理在应对LOTRCG不可预测性与挑战性时的适应能力与表现。论文同时探索了多智能体系统，通过为游戏不同决策维度分配独立强化学习代理。实验结果表明，该方法显著改善了游戏结果：强化学习代理在10,000局随机游戏中取得了78.5%的胜率。