## 1 Introduction  
Reinforcement learning is one of the most prominent research areas in artificial intelligence, playing a crucial role in developing agents that autonomously make decisions in complex environments [1]. In recent years, reinforcement learning has achieved remarkable success in various fields including gaming, robotics, and autonomous vehicles. Notably, the potential of reinforcement learning gained global attention when DeepMind's AlphaGo defeated the world's top Go player. Current research in reinforcement learning is evolving toward applications in more complex and realistic environments, multi-agent systems, and the development of safe and ethical AI [2]. Meanwhile, in the field of natural language processing, the advancement of large language models (LLMs) like GPT-3, BERT, and T5 has brought revolutionary changes [3, 4]. These models demonstrate performance close to or sometimes exceeding human capabilities in tasks such as text generation, translation, summarization, and question answering [5, 6]. The application areas for LLMs continue to expand, venturing into professional domains such as creative writing, code generation, medical diagnostic assistance, and legal document analysis [7, 8]. The development of LLMs has significantly enhanced AI's ability to understand and generate human language, opening new horizons for human-AI interaction [9, 10]. However, reinforcement learning and LLMs have evolved independently in their respective domains, and research on effectively combining the two technologies is still in its early stages. Integrating the vast knowledge and reasoning capabilities of LLMs into the decision-making processes of reinforcement learning agents presents a highly intriguing research topic. This approach goes beyond simply following predefined rules or policies, offering the potential to create intelligent agents capable of understanding situations and reasoning about appropriate actions. Thus, there is a growing need for novel approaches that apply LLMs to reinforcement learning. The purpose of this study is to optimize agent behavior in the MiniGrid environment using LLMs. MiniGrid is a simple yet versatile environment that provides various reinforcement learning tasks, making it suitable for objectively assessing agent performance. We aim to implement a process where the LLM receives the state of the environment as text input, understands it, and decides on the optimal action. Additionally, this study highlights the potential application domains where this integration of LLMs and reinforcement learning could be most impactful. Examples include robotics for precise manipulation, automation systems for efficient control, and autonomous vehicles for complex decision-making. Such applications demonstrate the adaptability and scalability of the proposed method in real-world scenarios. This will allow us to analyze how the language understanding and reasoning abilities of LLMs impact the performance enhancement of reinforcement learning agents. Specifically, this research will investigate how accurately LLMs can understand and interpret the state of the MiniGrid environment. We will examine what patterns of performance improvement are exhibited by agents using LLMs during the learning process. Additionally, this study will explore how the size of the LLM and the characteristics of its pre-trained data affect reinforcement learning performance. The structure of this paper is as follows: First, in the Related Work section, we introduce the basic concepts of reinforcement learning and LLMs, along with recent research trends. Next, the Methodology section details the specific approach for applying LLMs to the MiniGrid environment and the experimental design. In the Experimental Results and Analysis and Discussion sections, we present the experimental outcomes and analyze the performance and behavioral characteristics of LLM-based agents. Finally, the conclusion summarizes the main findings of the research and suggests directions for future studies.