  The emergence of large language models (LLMs) has substantially influenced
natural language processing, demonstrating exceptional results across various
tasks. In this study, we employ ``Introspective Tips" to facilitate LLMs in
self-optimizing their decision-making. By introspectively examining
trajectories, LLM refines its policy by generating succinct and valuable tips.
Our method enhances the agent's performance in both few-shot and zero-shot
learning situations by considering three essential scenarios: learning from the
agent's past experiences, integrating expert demonstrations, and generalizing
across diverse games. Importantly, we accomplish these improvements without
fine-tuning the LLM parameters; rather, we adjust the prompt to generalize
insights from the three aforementioned situations. Our framework not only
supports but also emphasizes the advantage of employing LLM in in-contxt
decision-making. Experiments involving over 100 games in TextWorld illustrate
the superior performance of our approach.
