## 5 Discussion
### Implications and Future Work  
The experimental results of this LLM-based reinforcement learning approach have several important implications.  
Firstly, this study empirically demonstrates that language models can effectively solve reinforcement learning problems, presenting a significant finding that highlights the potential for integrating natural language processing and reinforcement learning.  
Secondly, it is noteworthy that the LLM-based approach achieved reasonable performance even in complex environments such as MiniGrid-Empty-16x16-v0. This result showcases the LLM's strong generalization abilities and understanding of complex situations.  
Lastly, this study introduces a method of summarizing past experiences to inform current decision-making. This is an example of effectively applying the contextual understanding capabilities of LLMs to reinforcement learning, which could serve as a foundation for more advanced learning techniques in the future.  
However, addressing the challenge of unknown environments remains critical for reinforcement learning. While LLMs can easily relate to well-defined environments, such as a 5x5 grid system, their performance in unknown or dynamic environments requires additional exploration techniques. For instance, integrating hierarchical exploration strategies or active learning frameworks with LLMs could enhance their ability to adapt to unfamiliar settings. Future research should also consider designing prompts that encourage LLMs to hypothesize and test potential strategies in unknown environments, thereby improving exploration efficiency and robustness.  
The LLM-based reinforcement learning approach shows great potential, but there are several important limitations and directions for future research.  
Firstly, the key limitations of this approach include scalability, learning stability, and optimality. Applicability to larger-scale environments or real-world problems has not yet been thoroughly validated. Additionally, there is significant performance variability between episodes, making it challenging to ensure a stable learning curve. Moreover, the decisions made by the LLM do not always guarantee optimal actions, which is a critical drawback.  
To overcome these limitations and further advance the potential of LLM-based reinforcement learning, several research directions have been proposed. These include research that combines the advantages of LLMs with traditional reinforcement learning algorithms through hybrid approaches, the development of meta-learning strategies utilizing LLMs, and the exploration of prompt optimization techniques specifically tailored for reinforcement learning. Additionally, integrating multimodal information, improving sample efficiency, and developing explainable AI techniques are also recognized as important research tasks.  
In conclusion, while LLM-based reinforcement learning holds significant potential, it must overcome several technical challenges for practical applications. Future research should focus on effectively combining the language understanding capabilities of LLMs with the optimization techniques of reinforcement learning, with the aim of developing more intelligent and adaptive agents.