Programmatic policies for interpretable reinforcement learning using pre-trained models

• المؤلف الرئيسي: Tu, Xia Yang
• مؤلفون آخرون: Arvind Easwaran
• التنسيق: Final Year Project
• اللغة: English
• منشور في: Nanyang Technological University 2024
• الموضوعات: Computer and Information Science; LLM; Reinforcement learning
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/181169

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...