SOURCE-LINKED INTELLIGENCE

Improving Offline Goal-Conditioned Reinforcement Learning via Selective Reward Stimulation

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Goal-conditioned reinforcement learning aims to learn policies that reach specified goals, but remains challenging in offline settings with sparse rewards and long-horizon dependencies. In such settings, goal-completion information can be temporally distant from the early decisions that enable success, while offline value estimation introduces additional error. We study this issue from a reward-propagation perspective and show, in a stylized delayed-goal setting, how goal-directed value separation can become small relative to local estimation error. Motivated by this analysis, we propose Rewar

Read original source ↗ Open in workspace

recordType

paper

region

Global