Papers Hierarchical Reinforcement Learning
“Hierarchical Reinforcement Learning” 태그가 달린 논문 481편 · 필터 해제
Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space
Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating this behavior in quadrupedal robots has been a longstanding challenge. Here, we propose a hierarchical rei…
Hierarchical Reinforcement LearningEnhancing Social Intelligence in LLMs with Hierarchical Reasoning and Utterance-Level Goal Rewarding
Large language models (LLMs) excel in structured tasks but struggle with dynamic social interactions, where success requires long-term goal coordination and rapid adaptation. Current methods often apply uniform goal-base…
Hierarchical Reinforcement LearningHierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning
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 …
Hierarchical Reinforcement LearningContinuous ControlTwo-Timescale Hierarchical Reinforcement Learning for Resilient Operations
Unexpected shocks recur in global operations, requiring decision rules that adapt as market and operating conditions change. Many operational systems also have hierarchical structures in which long-term and short-term de…
Hierarchical Reinforcement LearningRAVEN: Reinforcement-Adaptive Visibility-Graph Planning for Robust Humanoid Navigation with Collision-Free MPC
Humanoid navigation in dynamic environments requires long-horizon planning while respecting short-horizon dynamic and safety constraints. Classical visibility-graph planners combined with model predictive control (MPC) c…
Hierarchical Reinforcement LearningHiFuzz: Hierarchical Reinforcement Learning for Semantic-Aware and Adaptive CPU Fuzzing
Modern processor verification struggles to reach deep architectural states due to the inefficiencies of traditional mutation-based fuzzing. We propose HiFuzz, a novel hierarchical reinforcement learning framework that re…
Hierarchical Reinforcement LearningHierarchical Reinforcement Learning in StarCraft Micromanagement with Influence Maps and Cluster-based Scripts
Real-time strategy (RTS) games present significant AI challenges, characterized by expansive state-action spaces arising from multi-unit coordination in continuous battlefields, and sparse delayed rewards stemming from f…
Hierarchical Reinforcement LearningHierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural networks. The framework decomposes the compression search across two leve…
Hierarchical Reinforcement LearningNeural Network CompressionActive LearningHierarchical Reinforcement Learning for Sparse-Reward Search in Commutative Algebra
Applying machine learning techniques to solving long-standing mathematical conjectures can be particularly challenging due to their extreme reward sparsity. As an illustrative example, we consider Kalai's algebraic Hirsc…
Hierarchical Reinforcement LearningGraph Neural NetworkSelect-to-Act: Hierarchical Reinforcement Learning via Adaptive Language Guidance
Reinforcement Learning (RL) has been widely applied to sequential decision-making, yet it often suffers from poor sample efficiency due to costly interactions with the environment. A limited line of recent work has start…
Hierarchical Reinforcement LearningImagine to Ensure Safety in Hierarchical Reinforcement Learning
This work investigates the safe exploration problem in reinforcement learning, where an agent must maximize cumulative performance while simultaneously satisfying safety constraints. This challenge becomes even more pron…
Hierarchical Reinforcement LearningHIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning
While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degrades in multi-turn long-horizon agentic tasks. Existing methods have…
Hierarchical Reinforcement LearningTowards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end-to-end Deep Reinforcement…
Hierarchical Reinforcement LearningMotion PlanningCooperative Long Rope Skipping via Multi-Agent Reinforcement Learning
Humans exhibit remarkable motor agility, enabling a wide range of dynamic skills such as running and jumping, which highlights the great potential of humanoid robots for athletic locomotion. Among athletic sports, long r…
Hierarchical Reinforcement LearningMulti-agent Reinforcement LearningAffordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation
The object manipulation capabilities of quadruped robots is an open research challenge. While previous studies have focused on low-level policy learning, task execution still relies on expert-designed high-level trajecto…
Hierarchical Reinforcement LearningNeetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models
Purpose The WHO's COVID-19 non-pharmaceutical interventions (e.g., lockdowns, vaccinations) effectively curb transmission but impose heavy economic strains. Existing research often neglects individual behaviors and false…
Hierarchical Reinforcement LearningDeconstructing Spatial Complexity: Hierarchical Decomposition for LLM Spatial Reasoning
LLMs have shown remarkable proficiency in general language understanding and reasoning. However, they consistently underperform in spatial reasoning that severely limits their application, particularly in embodied intell…
Hierarchical Reinforcement LearningSpatial ReasoningExploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL
Hierarchical Reinforcement Learning (HRL) promises to solve long-horizon Reinforcement Learning (RL) tasks more efficiently than non-hierarchical counterparts by discovering and reusing temporally-extended skills. Howeve…
Hierarchical Reinforcement LearningAdaptive Human-AI Coordination via Hierarchical Action Disentanglement
Human-AI collaboration requires agents that can adapt to diverse partner behaviors and skill levels while remaining robust to unseen partners. Existing methods often collapse to a single dominant behavior or learn poorly…
Hierarchical Reinforcement LearningPartner-Aware Hierarchical Skill Discovery for Robust Human-AI Collaboration
Multi-agent collaboration, especially in human-AI teaming, requires agents that can adapt to novel partners with diverse and dynamic behaviors. Conventional Deep Hierarchical Reinforcement Learning (DHRL) methods focus o…
Hierarchical Reinforcement Learning