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 encapsulate an extensive amount of world knowledge%2C 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%2C one interesting avenue that shows untapped potential is the use of LLMs as Reinforcement Learning (RL) agents to enable conversational RL problem solving. Therefore%2C in this study%2C 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%2C we leverage the introduced prompting technique for episode simulation and Q-Learning%2C facilitated by LLMs. We then show the practicality of our approach through two detailed case studies for Research Scientist and Legal Matter Intake workflows.?quality=80&w=800)
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