Large Language Models (LLMs) encapsulate an extensive amount of world knowledge, and this has enabled their application in various domains to improve the performance of a variety of Natural Language Processing (NLP) tasks. This has also facilitated a more accessible paradigm of conversation-based interactions between humans and AI systems to solve intended problems. However, one interesting avenue that shows untapped potential is the use of LLMs as Reinforcement Learning (RL) agents to enable conversational RL problem solving. Therefore, in this study, we explore the concept of formulating Markov Decision Process-based RL problems as LLM prompting tasks. We demonstrate how LLMs can be iteratively prompted to learn and optimize policies for specific RL tasks. In addition, we leverage the introduced prompting technique for episode simulation and Q-Learning, facilitated by LLMs. We then show the practicality of our approach through two detailed case studies for "Research Scientist" and "Legal Matter Intake" workflows.

翻译：大语言模型（LLMs）封装了广泛的世界知识，这使其能够应用于多个领域，提升各类自然语言处理（NLP）任务的性能。同时，这也促进了人类与AI系统之间基于对话的更易交互的范式，以解决预期问题。然而，一个尚未充分挖掘的潜在方向是将大语言模型用作强化学习（RL）智能体，以实现基于对话的强化学习问题求解。因此，在本研究中，我们探索了将基于马尔可夫决策过程的强化学习问题表述为大语言模型提示任务的构想。我们展示了如何通过迭代提示大语言模型来学习并优化特定强化学习任务的策略。此外，我们利用所引入的提示技术，借助大语言模型实现了情节模拟与Q学习。随后，我们通过“研究科学家”和“法律事务处理”工作流的两个详细案例研究，证明了该方法的实用性。