Summary of Reinforcement Learning Problem Solving with Large Language Models, by Sina Gholamian et al.
Reinforcement Learning Problem Solving with Large Language Models
by Sina Gholamian, Domingo Huh
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have shown great potential in various Natural Language Processing (NLP) tasks, enabling human-AI conversation-based interactions. However, the use of LLMs as Reinforcement Learning (RL) agents for conversational RL problem solving remains unexplored. This study formulates Markov Decision Process-based RL problems as LLM prompting tasks and demonstrates how LLMs can be iteratively prompted to learn and optimize policies for specific RL tasks. The proposed approach is then applied to two case studies: “Research Scientist” and “Legal Matter Intake” workflows, showcasing the practicality of our method. We leverage the introduced prompting technique for episode simulation and Q-Learning, facilitated by LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how Large Language Models (LLMs) can help solve problems by having conversations with AI systems. Normally, we use these models to improve language tasks like chatbots or language translation. But what if we could use them as “problem solvers” too? This is exactly what this research does. The authors turn complex problem-solving into a conversation between humans and AI, making it easier to solve certain types of problems. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Prompting » Reinforcement learning » Translation
