Author

N. Khraief

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Jul 2026

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

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.

Zahra Abdalla Elashaal, Afef Hfaiedh, N. Khraief et al. · arXiv.org · 0 citations