aiqbench analysis
This research introduces G2QDR, a framework that leverages directed state graphs to generate dense auxiliary rewards for goal-conditioned hierarchical reinforcement learning in asymmetric environments. By modeling intrinsic connectivity rather than using graphs merely as sampling tools, the method improves policy stability and path planning efficiency across sparse reward tasks. The study demonstrates that this approach can be integrated into existing architectures with manageable computational overhead, offering a practical advancement for complex RL applications.
From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs
arXiv:2609.10781v1 Announce Type: new Abstract: The integration of graphs with Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) has received increasing attention, as graphs naturally encode task hierarchies for effective subgoal sampling. However, existing methods often overlook intrinsic connectivity information, failing to fully leverage the underlying topology for efficient learning. Most graph-based GCHRL methods use the graph as a stochastic sampling tool rather than as an environmental model that encodes connectivity and state-accessibility information. This limitation is particularly acute in quasimetric environments, where the inherent asymmetry of state transitions poses a fundamental challenge to stable policy learning and robust path planning. In this paper, we address these problems by introducing a state connectivity model designed to predict pairwise state connectivity strength in asymmetric environments. We transform these connectivity strengths into scalar auxiliary dense rewards, providing continuous guidance across multiple hierarchical levels. We demonstrate that our proposed framework, Graph-Guided Quasimetric Dense Reward (G2QDR), can theoretically be integrated into any existing GCHRL architecture, and the state connectivity model is efficiently implemented via a neural network trained on a directed state graph generated during exploration. Empirical results across a wide range of sparse reward environments indicate that, in general, G2QDR can enhance the performance of baseline GCHRL approaches with acceptable computational overhead.
Abstract:The integration of graphs with Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) has received increasing attention, as graphs naturally encode task hierarchies for effective subgoal sampling. However, existing methods often overlook intrinsic connectivity information, failing to fully leverage the underlying topology for efficient learning. Most graph-based GCHRL methods use the graph as a stochastic sampling tool rather than as an environmental model that encodes connectivity and state-accessibility information. This limitation is particularly acute in quasimetric environments, where the inherent asymmetry of state transitions poses a fundamental challenge to stable policy learning and robust path planning. In this paper, we address these problems by introducing a state connectivity model designed to predict pairwise state connectivity strength in asymmetric environments. We transform these connectivity strengths into scalar auxiliary dense rewards, providing continuous guidance across multiple hierarchical levels. We demonstrate that our proposed framework, Graph-Guided Quasimetric Dense Reward (G2QDR), can theoretically be integrated into any existing GCHRL architecture, and the state connectivity model is efficiently implemented via a neural network trained on a directed state graph generated during exploration. Empirical results across a wide range of sparse reward environments indicate that, in general, G2QDR can enhance the performance of baseline GCHRL approaches with acceptable computational overhead.
Machine Learning (cs.LG)
arXiv:2609.10781 [cs.LG]
(or arXiv:2609.10781v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.10781 arXiv-issued DOI via DataCite (pending registration)
Zhang, S., Wang, Z., Chang, X.-W., & Precup, D. (2026). From connectivity to rewards: Dense reward learning with directed state graphs. Transactions on Machine Learning Research. https://openreview.net/forum?id=F65zrefsjB
Full text under the publisher's open licence · 246 words
Related coverage
PIRAMID: Progress and Plans
LessWrong · Jul 27, 2026
LocusRL: Diagnosing LLM Reward and Policy Interventions in Competitive Games
arXiv · Oct 6, 2026
[AINews] Reality Checks on AI News (Yegge shuts down Gas Town, Databricks’ +60% Astra cost)
Latent Space · Sep 17, 2026
Reflections on unlearning and inoculation
LessWrong · Sep 20, 2026