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

• URL: http://arxiv.org/abs/2607.23726v1
• Date: Sun, 26 Jul 2026 15:41:52 GMT
• Title: Hierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning
• Abstract summary: The proposed framework was trained and evaluated using the Search-and-Rescue-2 dataset.<n>HRL-SAC effectively addresses sparse-reward long-horizon search problems characterized by delayed rewards and continuous control.<n>These findings indicate that hierarchical entropy-regularized policies are a promising solution to tackle long-horizon sparse-reward reinforcement learning tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• QoS-Aware Hierarchical Reinforcement Learning for Joint Link Selection and Trajectory Optimization in SAGIN-Supported UAV Mobility Management [52.15690855486153]
  A space-air-ground integrated network (SAGIN) has emerged as an essential architecture for enabling ubiquitous UAV connectivity.<n>This paper formulates UAV mobility management in SAGIN as a constrained multiobjective joint optimization problem.
  arXiv  Detail & Related papers  (2025-12-17T06:22:46Z)
• Reinforcement Learning with Anticipation: A Hierarchical Approach for Long-Horizon Tasks [3.79187263097166]
  Solving long-horizon goal-conditioned tasks remains a significant challenge in reinforcement learning.<n>We introduce Reinforcement Learning with Anticipation (RLA), a principled and potentially scalable framework designed to address these limitations.<n>Key feature of RLA is the training of the anticipation model, which is guided by a principle of value geometric consistency.
  arXiv  Detail & Related papers  (2025-09-06T00:10:15Z)
• Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning [56.496001894673235]
  Reinforcement Learning (RL) has proven highly effective at enhancing the complex reasoning abilities of Large Language Models (LLMs)<n>Our analysis reveals that puzzling phenomena like aha moments", length-scaling'' and entropy dynamics are not disparate occurrences but hallmarks of an emergent reasoning hierarchy.
  arXiv  Detail & Related papers  (2025-09-03T18:52:49Z)
• Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning [5.274804664403783]
  Strict Subgoal Execution (SSE) is a graph-based hierarchical RL framework that enforces single-step subgoal reachability.<n>We show that SSE consistently outperforms existing goal-conditioned RL and hierarchical RL approaches in both efficiency and success rate.
  arXiv  Detail & Related papers  (2025-06-26T06:35:42Z)
• Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning [32.260964481673085]
  Large language models (LLMs) struggle with long-horizon decision-making tasks due to deficient exploration and long-term credit assignment.<n>We propose an innovative framework that introduces a parameter-efficient and generally applicable hierarchy to LLM policies.<n>We develop a scheme where the low-level controller is supervised with abstract, step-by-step plans that are learned and instructed by the high-level policy.
  arXiv  Detail & Related papers  (2025-05-26T09:43:40Z)
• Direct Preference Optimization for Primitive-Enabled Hierarchical Reinforcement Learning [75.9729413703531]
  DIPPER is a novel HRL framework that formulates hierarchical policy learning as a bi-level optimization problem.<n>We show that DIPPER achieves up to 40% improvement over state-of-the-art baselines in sparse reward scenarios.
  arXiv  Detail & Related papers  (2024-11-01T04:58:40Z)
• Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning [56.26889258704261]
  We propose a novel "disentangled" decision-making method, Semantically Aligned task decomposition in MARL (SAMA) SAMA prompts pretrained language models with chain-of-thought that can suggest potential goals, provide suitable goal decomposition and subgoal allocation as well as self-reflection-based replanning. SAMA demonstrates considerable advantages in sample efficiency compared to state-of-the-art ASG methods.
  arXiv  Detail & Related papers  (2023-05-18T10:37:54Z)
• Efficient Learning of High Level Plans from Play [57.29562823883257]
  We present Efficient Learning of High-Level Plans from Play (ELF-P), a framework for robotic learning that bridges motion planning and deep RL. We demonstrate that ELF-P has significantly better sample efficiency than relevant baselines over multiple realistic manipulation tasks.
  arXiv  Detail & Related papers  (2023-03-16T20:09:47Z)
• HAC Explore: Accelerating Exploration with Hierarchical Reinforcement Learning [8.889563735540696]
  We propose HAC Explore (HACx), a new method that combines the exploration bonus method Random Network Distillation (RND) into the hierarchical approach Hierarchical Actor-Critic (HAC) HACx is the first RL method to solve a sparse reward, continuous-control task that requires over 1,000 actions.
  arXiv  Detail & Related papers  (2021-08-12T17:42:12Z)
• SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning [102.78958681141577]
  We present SUNRISE, a simple unified ensemble method, which is compatible with various off-policy deep reinforcement learning algorithms. SUNRISE integrates two key ingredients: (a) ensemble-based weighted Bellman backups, which re-weight target Q-values based on uncertainty estimates from a Q-ensemble, and (b) an inference method that selects actions using the highest upper-confidence bounds for efficient exploration.
  arXiv  Detail & Related papers  (2020-07-09T17:08:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.