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arXiv Machine Learning By Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup

From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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