From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs
Updated 35 min ago · first seen 11 Sept 2026
paper_01M294FNSB4K0Z49Z2F8XQ4CBT
- Published
- 11 Sept 2026
- T1 · 35 min ago
- arXiv
- 2609.10781
- T1 · 35 min ago
- Category
- cs.LG
- T1 · 35 min ago
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.
Authors 4
Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
- arXiv id
- 2609.10781
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
- Categories
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
- Primary category
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
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Provenance
Attributed facts
9
Source tiers
T19
Freshest observation
35 min ago
Conflicts
None
No models linked to this paper yet.
- Authors
- Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang
As of
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 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. | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 35 min ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.