Incorporating Spatial Information into Goal-Conditioned Hierarchical Reinforcement Learning via Graph Representations

• URL: http://arxiv.org/abs/2511.10872v1
• Date: Fri, 14 Nov 2025 00:58:39 GMT
• Title: Incorporating Spatial Information into Goal-Conditioned Hierarchical Reinforcement Learning via Graph Representations
• Authors: Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup,
• Abstract summary: The integration of graphs with Goal-conditioned Reinforcement Learning (GCHRL) has recently gained attention.<n>Existing approaches typically rely on domain-specific knowledge to construct these graphs.<n>This paper proposes a solution by developing a graph encoder-decoder to evaluate unseen states.
• Score: 37.10671332775445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of graphs with Goal-conditioned Hierarchical Reinforcement Learning (GCHRL) has recently gained attention, as intermediate goals (subgoals) can be effectively sampled from graphs that naturally represent the overall task structure in most RL tasks. However, existing approaches typically rely on domain-specific knowledge to construct these graphs, limiting their applicability to new tasks. Other graph-based approaches create graphs dynamically during exploration but struggle to fully utilize them, because they have problems passing the information in the graphs to newly visited states. Additionally, current GCHRL methods face challenges such as sample inefficiency and poor subgoal representation. This paper proposes a solution to these issues by developing a graph encoder-decoder to evaluate unseen states. Our proposed method, Graph-Guided sub-Goal representation Generation RL (G4RL), can be incorporated into any existing GCHRL method when operating in environments with primarily symmetric and reversible transitions to enhance performance across this class of problems. We show that the graph encoder-decoder can be effectively implemented using a network trained on the state graph generated during exploration. Empirical results indicate that leveraging high and low-level intrinsic rewards from the graph encoder-decoder significantly enhances the performance of state-of-the-art GCHRL approaches with an extra small computational cost in dense and sparse reward environments.

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