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arXiv · 2505.21750

Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals

Vivienne Huiling Wang·Tinghuai Wang·Joni Pajarinen
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Abstract

Hierarchical reinforcement learning (HRL) learns to make decisions on multiple levels of temporal abstraction. A key challenge in HRL is that the low-level policy changes over time, making it difficult for the high-level policy to generate effective subgoals. To address this issue, the high-level policy must capture a complex subgoal distribution while also accounting for uncertainty in its estimates. We propose an approach that trains a conditional diffusion model regularized by a Gaussian Process (GP) prior to generate a complex variety of subgoals while leveraging principled GP uncertainty quantification. Building on this framework, we develop a strategy that selects subgoals from both the diffusion policy and GP's predictive mean. Our approach outperforms prior HRL methods in both sample efficiency and performance on challenging continuous control benchmarks.

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Vivienne Huiling Wang, Tinghuai Wang, Joni Pajarinen. 2025-05-27. Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals. https://arxiv.org/abs/2505.21750

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