Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning
arXiv:2506.21039v3 Announce Type: replace 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 adm
The paper addresses a core limitation in current hierarchical reinforcement learning, a field rapidly evolving to tackle complex, long-horizon AI tasks.
Improving reliable long-horizon planning is crucial for developing general-purpose AI agents capable of performing multi-step, real-world tasks effectively.
The proposed Strict Subgoal Execution (SSE) framework enhances the robustness and efficiency of hierarchical RL, potentially accelerating the development of more capable AI systems.
- · AI agents developers
- · Robotics research
- · Industries requiring complex automation
- · Current inefficient hierarchical RL methods
More robust and efficient AI agents for complex task execution.
Accelerated deployment of AI in sectors requiring sequential decision-making and long-term planning.
Increased investor interest and R&D spend in advanced AI autonomy and agentic systems.
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Read at arXiv cs.LG