Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals

• URL: http://arxiv.org/abs/2505.21750v1
• Date: Tue, 27 May 2025 20:38:44 GMT
• Title: Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals
• Authors: Vivienne Huiling Wang, Tinghuai Wang, Joni Pajarinen,
• Abstract summary: 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.<n>We propose an approach that trains a conditional diffusion model regularized by a Gaussian Process (GP) prior to generate a complex variety of subgoals.<n>Building on this framework, we develop a strategy that selects subgoals from both the diffusion policy and GP's predictive mean.
• Score: 12.894271401094615
• License: http://creativecommons.org/licenses/by/4.0/
• 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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