On the Planning, Search, and Memorization Capabilities of Large Language Models

The rapid advancement of large language models, such as the Generative Pre-trained Transformer (GPT) series, has had significant implications across various disciplines. In this study, we investigate the potential of the state-of-the-art large language model (GPT-4) for planning tasks. We explore its effectiveness in multiple planning subfields, highlighting both its strengths and limitations. Through a comprehensive examination, we identify areas where large language models excel in solving planning problems and reveal the constraints that limit their applicability. Our empirical analysis focuses on GPT-4's performance in planning domain extraction, graph search path planning, and adversarial planning. We then propose a way of fine-tuning a domain-specific large language model to improve its Chain of Thought (CoT) capabilities for the above-mentioned tasks. The results provide valuable insights into the potential applications of large language models in the planning domain and pave the way for future research to overcome their limitations and expand their capabilities.Comment: 13 pages, 2 figure

A Survey on Reinforcement Learning for Combinatorial Optimization

This paper gives a detailed review of reinforcement learning in combinatorial optimization, introduces the history of combinatorial optimization starting in the 1960s, and compares it with the reinforcement learning algorithms in recent years. We explicitly look at a famous combinatorial problem known as the Traveling Salesperson Problem (TSP). We compare the approach of the modern reinforcement learning algorithms on TSP with an approach published in 1970. Then, we discuss the similarities between these algorithms and how the approach of reinforcement learning changes due to the evolution of machine learning techniques and computing power. We also mention the deep learning approach on the TSP, which is named Deep Reinforcement Learning. We argue that deep learning is a generic approach that can be integrated with traditional reinforcement learning algorithms and optimize the outcomes of the TSP.Comment: manuscript submitted to Management Scienc