Programmatic policies for interpretable reinforcement learning using pre-trained models - Tu, Xia Yang (Nanyang Technological University, 2024)
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 programmatic policies through large language models (LLMs). Initially looking at decision trees within latent space representation, we proceed to develop and apply a “code reflection” framework in the Karel environment, which provides a controlled setting for evaluating task performance. The framework leverages prompt engineering and code reflection to optimize program synthesis, producing interpretable policies. The report discusses the findings and concludes with recommendations for future work at the end.