arXiv Machine Learning
PathBridger: Subgoal Bridges for Offline Goal-Conditioned Reinforcement Learning
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Offline RL with Hierarchical Action Chunking
arXiv:2607. 20834v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets.
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Offline RL with Hierarchical Action Chunking
Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets. However, scaling these methods to long-horizon tasks remains a challenge due to the curse of horizon, where value estimation errors can compound through long chains of bootstrapped Bellman backups.
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SEAR: Sample Efficient Action Chunking Reinforcement Learning
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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.
arXiv AI
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Q-Shaped Options for Hierarchical Reinforcement Learning
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