Programmatic policies for interpretable reinforcement learning using pre-trained models

• Main Author: Tu, Xia Yang
• Other Authors: Arvind Easwaran
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Computer and Information Science; LLM; Reinforcement learning
• Online Access: https://hdl.handle.net/10356/181169
• Institution: Nanyang Technological University

Decision Trees (DTs) are widely used in machine learning due to their critical interpretability. However, training DTs in a Reinforcement Learning (RL) setting is challenging. In the project, we present a framework to improve the interpretability of reinforcement learning (RL) by generating prog...