2025
Efficient Exploitation of Hierarchical Structure in Sparse Reward Reinforcement Learning
Gianluca Drappo, Arnaud Robert, Marcello Restelli, Aldo A. Faisal, Alberto Maria Metelli, Ciara Pike-Burke
AISTATS 2025poster
We study goal-conditioned Hierarchical Reinforcement Learning (HRL), where a high-level agent instructs sub-goals to a low-level agent. Under the assumption of a sparse reward function and known hierarchical decomposition, we propose a new algorithm to learn optimal hierarchical policies. Our algori…