## 6 Conclusions  
This experimental setup presents a novel approach to applying the capabilities of language models to reinforcement learning problems. The use of a model specialized in the Korean language offers an opportunity to explore the impact of linguistic characteristics on reinforcement learning performance. This is expected to provide valuable insights for the development of AI agents in more complex gaming environments or real-world problems in the future. The key aspect of this study is the integration of LLMs to combine complex decision-making processes with natural language processing capabilities. The method of describing environmental states in natural language and inputting them into the LLM for inferring appropriate actions differentiates this approach from existing reinforcement learning methodologies. This enables the agent to make decisions based on a high-level understanding of the environment and demonstrates the potential to enhance generalization capabilities, especially in complex situations. The LLM-based reinforcement learning approach proposed in this study demonstrates significant potential while also revealing several important strengths and limitations. Among its strengths, firstly, is the ability of LLMs to leverage their exceptional language understanding capabilities to effectively process reinforcement learning environment states. This suggests the possibility of flexible application to a wide range of reinforcement learning problems. Secondly, LLMs show excellent generalization performance based on pre-trained knowledge, leading to faster learning and higher success rates in new situations. However, key limitations include scalability and optimality issues. The applicability of the approach to larger-scale environments or real-world problems has not yet been fully validated. Additionally, while the model achieves high success rates in reaching goals, the variability in performance across episodes makes it challenging to ensure a stable learning curve. Considering these strengths and limitations, future research should involve a deeper analysis of the effectiveness of this approach through performance evaluation in more complex environments, comparisons among various LLMs, and assessments against traditional reinforcement learning algorithms. This includes studies on hybrid approaches that integrate the strengths of LLMs and traditional reinforcement learning algorithms, the development of meta-learning strategies utilizing LLMs, and the exploration of prompt optimization techniques tailored to reinforcement learning. Furthermore, refining the inference processes of the LLM and implementing prompt engineering tailored to environmental characteristics will further improve performance. Additionally, integrating multimodal information, improving sample efficiency, and developing explainable AI techniques will be essential to overcoming current limitations. By maximizing the advantages of LLM-based reinforcement learning and addressing its shortcomings, researchers can expect to enable the development of more intelligent and adaptive agents. This research direction presents a new paradigm in the field of reinforcement learning and could significantly contribute to solving complex real-world problems. Conceptualization, B.-J.P. and S.-J.Y. ; methodology, B.-J.P. and S.-J.Y. ; software, B.-J.P. and S.-J.Y. ; data curation, B.-J.P., S.-J.Y. and H.-S.H. ; investigation, S.-J.Y. and H.-S.H. ; writing--original draft preparation, B.-J.P. and H.-S.H. ; writing--review and editing, B.-J.P., S.-J.Y. and H.-S.H. ; supervision, I.-Y.M. All authors have read and agreed to the published version of the manuscript. This research was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-004). Not applicable. **Data Availability Statement:** Publicly available LLM, were used in this study.