The paper introduces Graph-Guided Quasimetric Dense Reward (G2QDR), a framework that learns a state connectivity model to predict pairwise connectivity strengths in asymmetric environments. These strengths are converted into scalar auxiliary dense rewards, offering continuous guidance across hierarchical levels. G2QDR can be integrated into any existing Goal-Conditioned Hierarchical Reinforcement Learning architecture and shows empirical performance improvements in sparse reward settings with modest computational cost.
By Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup
arXiv:2606. 06673v1 Announce Type: new Abstract: Sparse rewards and heterogeneous task sequences remain persistent challenges in Reinforcement Learning (RL), often resulting in slow convergence, weak generalization, and inefficient exploration.
By Ujjwal Bhatta, Utsabi Dangol, Sumaly Bajracharya, Rodrigue Rizk, KC Santosh
arXiv:2608.29061v1 Announce Type: new
Abstract: Offline goal-conditioned reinforcement learning (GCRL) aims to learn policies for reaching diverse goals entirely from fixed trajectory data. Long-hori...
By Soohyun Choi, Seonvin Cho, Songnam Hong
arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.
By Eric Elmoznino, Sangnie Bhardwaj, Johannes von Oswald, Rajai Nasser, Blaise Ag\"uera y Arcas, Jo\~ao Sacramento, Rif A. Saurous, Guillaume Lajoie
arXiv:2609.05650v1 Announce Type: new
Abstract: We propose a reinforcement learning framework in which exploration is driven by intrinsic curiosity, designed for scenarios where environments are non-...
By Armando Vieira
arXiv:2607. 23726v1 Announce Type: cross Abstract: Exploration in sparse-reward long-horizon tasks poses significant challenges for reinforcement learning.
By Zahra Abdalla Elashaal, Afef Hfaiedh, Nahla Khraief, Issmail Ellabib, Giansalvo Cirrincione