HorizonFlow: Variable-Length Planning for Offline Goal-Conditioned RL
Quick summary
arXiv:2609.36896v1 Announce Type: new Abstract: Recent advances in generative planning have made trajectory inpainting a promising approach to offline goal-conditioned reinforcement learning. However, these methods typically specify the planning horizon before generating plan content, even though the appropriate horizon depends on the route itself. A horizon that is too short can force infeasible transitions, whereas one that is too long can introduce redundant motion. We introduce HorizonFlow, a hierarchical planner that treats plan length as an output of generation rather than a prescribed i
Key takeaways
- arXiv:2609.36896v1 Announce Type: new Abstract: Recent advances in generative planning have made trajectory inpainting a promising approach to offline goal-conditioned reinforcement learning.
- However, these methods typically specify the planning horizon before generating plan content, even though the appropriate horizon depends on the route itself.
- A horizon that is too short can force infeasible transitions, whereas one that is too long can introduce redundant motion.
Why it matters
The importance of “HorizonFlow: Variable-Length Planning for Offline Goal-Conditioned RL” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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