Integrating Large Language Models with Reinforcement Learning for Generalization in Strategic Card Games:Extended Abstract

Strategic card games, such as Hearthstone, offer a rich environment for exploring decision-making in reinforcement learning (RL). Yet, achieving generalization across diverse and evolving game scenarios remains a significant challenge. In this paper, we introduce a novel framework that integrates large language models (LLMs) with RL agents to improve generalization in strategic card games. Our approach leverages a fine-tuned T5 model to encode and interpret card strategies expressed in natural language, facilitating efficient policy learning across a large and continually expanding set of cards. Employing a self-play RL framework augmented with an auxiliary transition loss in the latent space, our agent captures and generalizes the complex, dynamic nature of card interactions. Experimental results show that our method not only enhances learning efficiency but also significantly improves the agent's ability to generalize, maintaining robust performance when encountering new cards.