From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs
Existing graph-based GCHRL methods treat graphs as stochastic sampling tools rather than environmental models encoding connectivity and state-accessibility information The paper introduces a state connectivity model that predicts pairwise state connectivity strength in asymmetric (quasimetric) environments Connectivity strengths are transformed into scalar auxiliary dense rewards, providing continuous guidance across multiple hierarchical levels The proposed G2QDR framework is theoretically inte
Analysis
TL;DR
- Existing graph-based GCHRL methods treat graphs as stochastic sampling tools rather than environmental models encoding connectivity and state-accessibility information
- The paper introduces a state connectivity model that predicts pairwise state connectivity strength in asymmetric (quasimetric) environments
- Connectivity strengths are transformed into scalar auxiliary dense rewards, providing continuous guidance across multiple hierarchical levels
- The proposed G2QDR framework is theoretically integrable into any existing GCHRL architecture with acceptable computational overhead
- Empirical results across sparse reward environments show G2QDR consistently enhances baseline GCHRL performance
Why It Matters
This work addresses a critical gap in hierarchical reinforcement learning where graph-based methods underutilize topological information, particularly in asymmetric environments where state transitions are non-symmetric. For AI practitioners working on complex navigation, robotics, or sequential decision-making tasks with sparse rewards, G2QDR offers a plug-and-play enhancement that can significantly improve sample efficiency and learning stability without requiring architectural overhauls.
Technical Details
- Problem setting: Quasimetric environments where state transition asymmetry creates fundamental challenges for stable policy learning and robust path planning in GCHRL
- State connectivity model: A neural network trained on a directed state graph generated during exploration, predicting pairwise connectivity strength between states in asymmetric environments
- Reward transformation: Pairwise connectivity strengths are converted into scalar auxiliary dense rewards that provide continuous guidance across multiple hierarchical levels of the GCHRL framework
- Framework design: G2QDR (Graph-Guided Quasimetric Dense Reward) is designed as a modular component theoretically integrable into any existing GCHRL architecture
- Evaluation: Tested across a wide range of sparse reward environments, demonstrating performance improvements over baseline GCHRL approaches with acceptable computational overhead
Industry Insight
- The shift from treating graphs as mere sampling tools to environmental models represents a paradigm change that could influence how researchers design graph-based RL systems for complex, asymmetric environments
- The modularity of G2QDR means practitioners can adopt this approach incrementally, layering it onto existing GCHRL pipelines without complete reimplementation
- As robotic and autonomous systems increasingly operate in asymmetric environments (e.g., terrain navigation, traffic flow), methods that explicitly model state accessibility will become increasingly valuable for robust deployment
Disclaimer: The above content is generated by AI and is for reference only.