Natural Language-based State Representation in Deep Reinforcement Learning

Md Masudur Rahman, Yexiang Xue

Keywords: Deep Reinforcement Learning, Generalization, State Representation

TL;DR: This study examines the advantages of using natural language descriptions over direct image observations in reinforcement learning, showing improved interpretability and policy generalization leveraging large language models.

Abstract: This study investigates the potential of using natural language descriptions as an alternative to direct image-based observations for learning policies in reinforcement learning. Due to the inherent challenges in managing image-based observations, which include abundant information and irrelevant features, we propose a method that compresses images into a natural language form for state representation. This approach allows better interpretability and leverages the processing capabilities of large language models (LLMs). We conducted several experiments involving tasks that required image-based observation. The results demonstrated that policies trained using natural language descriptions of images yield better generalization than those trained directly from images, emphasizing the potential of this approach in practical settings.

Submission Number: 2