Hierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning

Zahra Abdalla Elashaal; Afef Hfaiedh; Nahla Khraief; Issmail Ellabib; Giansalvo Cirrincione

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arXiv · 2607.23726

Hierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning

Zahra Abdalla Elashaal·Afef Hfaiedh·Nahla Khraief·Issmail Ellabib·Giansalvo Cirrincione

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Abstract

Exploration in sparse-reward long-horizon tasks poses significant challenges for reinforcement learning. To address these challenges, we propose a two-level Hierarchical Reinforcement Learning (HRL) framework. The first level handles high-level strategic planning, while the low-level uses the continuous-control Soft Actor-Critic (SAC) algorithm, and they utilize entropy-regularized policy optimization. The proposed framework was trained and evaluated using the Search-and-Rescue-2 (SAR-2) dataset. HRL-SAC effectively addresses sparse-reward long-horizon search problems characterized by delayed rewards and continuous control, and its outperforming the flat SAC baseline reinforcement learning in terms of success rates, coverage efficiency, and convergence. These findings indicate that hierarchical entropy-regularized policies are a promising solution to tackle long-horizon sparse-reward reinforcement learning tasks.

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Zahra Abdalla Elashaal, Afef Hfaiedh, Nahla Khraief, Issmail Ellabib, Giansalvo Cirrincione. 2026-07-26. Hierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning. https://arxiv.org/abs/2607.23726

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