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[2506.21039] Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning
Summary
The paper presents Strict Subgoal Execution (SSE), a novel framework for hierarchical reinforcement learning that enhances long-horizon planning by improving subgoal reliability and decision-making efficiency.
Why It Matters
This research addresses significant challenges in reinforcement learning, particularly in long-horizon tasks where traditional methods struggle. By introducing SSE, the authors provide a solution that enhances the reliability of subgoals, which is crucial for developing more effective AI systems capable of complex decision-making.
Key Takeaways
- SSE improves high-level decision-making in hierarchical RL by integrating Frontier Experience Replay.
- The framework effectively separates reachable from unreachable subgoals, enhancing planning efficiency.
- Experimental results show SSE outperforms existing methods in both efficiency and success rates across various benchmarks.
Computer Science > Machine Learning arXiv:2506.21039 (cs) [Submitted on 26 Jun 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning Authors:Jaebak Hwang, Sanghyeon Lee, Jeongmo Kim, Seungyul Han View a PDF of the paper titled Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning, by Jaebak Hwang and 3 other authors View PDF HTML (experimental) Abstract:Long-horizon goal-conditioned tasks pose fundamental challenges for reinforcement learning (RL), particularly when goals are distant and rewards are sparse. While hierarchical and graph-based methods offer partial solutions, their reliance on conventional hindsight relabeling often fails to correct subgoal infeasibility, leading to inefficient high-level planning. To address this, we propose Strict Subgoal Execution (SSE), a graph-based hierarchical RL framework that integrates Frontier Experience Replay (FER) to separate unreachable from admissible subgoals and streamline high-level decision making. FER delineates the reachability frontier using failure and partial-success transitions, which identifies unreliable subgoals, increases subgoal reliability, and reduces unnecessary high-level decisions. Additionally, SSE employs a decoupled exploration policy to cover underexplored regions of the goal space and a path refinement that adjusts edge costs using observed low-level failure...
