Two-step reinforcement learning for multistage strategy card game

Abstract

This study introduces a two-step reinforcement learning (RL) strategy tailored for "The Lord of the Rings: The Card Game", a complex multistage strategy card game. The research diverges from conventional RL methods by adopting a phased learning approach, beginning with a foundational learning step 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 the unpredictable and challenging nature of the game. The paper also explores a multi-phase system where distinct RL agents are employed for various decision-making phases of the game. This approach has demonstrated remarkable improvement, with the RL agents achieving a winrate of 78.5 % at the highest difficulty level.