2 papers
cs.LG2024
Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search
Max Liu, Chan-Hung Yu, Wei-Hsu Lee +3
Programmatic reinforcement learning (PRL) has been explored for representing policies through programs as a means to achieve interpretability and generalization. Despite promising…
cs.LG2023
Program Machine Policy: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines
Yu-An Lin, Chen-Tao Lee, Guan-Ting Liu +2
Deep reinforcement learning (deep RL) excels in various domains but lacks generalizability and interpretability. On the other hand, programmatic RL methods (Trivedi et al., 2021; L…