Adaptive Coarse-to-Fine Subgoal Refinement for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
arXiv:2605.28127v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) is challenging in long-horizon tasks, where distant state--goal pairs provide weak supervision and value estimates become vulnerable to accumulated bootstrapping errors. Hierarchical methods mitigate this difficulty by introducing intermediate subgoals, but fixed temporal abstractions or fixed hierarchy depths can be mismatched to state--goal pairs with different reachability horizons. We propose Coarse-to-Fine Hierarchical Goal Reinforcement Learning (CFHRL), a fully offline GCRL framework t
The continuous drive for more robust and autonomous AI systems, especially in complex, real-world scenarios, pushes the development of more advanced reinforcement learning techniques.
This research addresses a core challenge in autonomous AI: enabling efficient learning and planning for long-horizon tasks, which is critical for practical agent deployment.
The ability of AI agents to break down complex goals into manageable subgoals more adaptively improves their learning efficiency and problem-solving capabilities in challenging environments.
- · AI research labs
- · Robotics companies
- · Developers of AI agents
- · Developers with overly simplistic goal-conditioned RL approaches
Improved performance and broader applicability of offline deep reinforcement learning systems.
Accelerated development of more capable and versatile AI agents across various domains, from industrial automation to personal assistants.
Increased automation of complex physical and digital tasks, potentially leading to significant shifts in labor markets and industrial processes.
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Read at arXiv cs.LG