Optimizing Agent Behavior in the MiniGrid Environment Using Reinforcement Learning Based on Large Language Models

Reinforcement learning is one of the most prominent research areas in the field of artificial intelligence, playing a crucial role in developing agents that autonomously make decisions in complex environments. This study proposes a method to optimize agent behavior in the MiniGrid-Empty-5x5-v0 environment using large language models (LLMs). By leveraging the natural language processing capabilities of LLMs to interpret environmental states and select appropriate actions, this research explores an approach that differs from traditional reinforcement learning methods. Experimental results confirm that LLM-based agents can effectively achieve their goals, and it is anticipated that maximizing the synergy between LLMs and reinforcement learning will contribute to the development of more intelligent and adaptable AI systems.