arXiv AI
4d ago

HorizonFlow: Variable-Length Planning for Offline Goal-Conditioned RL

HorizonFlow is a hierarchical planner for offline goal-conditioned reinforcement learning that treats the planning horizon as an output rather than a fixed input. It uses a subgoal route planner and an action-prefix controller, both employing insertion-based generation and flow matching, to jointly generate continuous plan content and its length. The method leverages the partially generated plan to guide token insertion and to steer generation toward shorter plans, achieving superior performance on Maze2D, Multi2D, and OGBench benchmarks.

By JunHyeok Oh, Zian Jang, Byung-Jun Lee