Results 111 to 120 of about 404,230 (315)
Online hierarchical reinforcement learning based on interrupting Option
Tongxin xuebao, 2016 Aiming at dealing with volume of big data,an on-line updating algorithm,named by Macro-Q with in-place updating (MQIU),which was based on Macro-Q algorithm and takes advantage of in-place updating approach,was proposed.The MQIU algorithm updates both the
Fei ZHU +4 more
doaj +2 more sources
Hierarchical Reinforcement Learning with Hindsight
, 2018 Duplicate.
Levy, Andrew +2 more
openaire +2 more sources
Bridging Optical and Mechanical Metamaterial/Metasurface Realms Toward Integrated Meta‐Systems
Advanced Optical Materials, EarlyView.This perspective describes the rise of metamaterials in the field of materials science, specifically with optical and mechanical functionality. Fundamentals of both optical and mechanical metamaterials are discussed with a review of state‐of‐the‐art metamaterial science.
Justin Brackenridge +2 more
wiley +1 more source
Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space
Advanced Robotics Research, EarlyView.A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo +6 more
wiley +1 more source
Hierarchical deep reinforcement learning for self-adaptive economic dispatch
HeliyonIt is challenging to accurately model the overall uncertainty of the power system when it is connected to large-scale intermittent generation sources such as wind and photovoltaic generation due to the inherent volatility, uncertainty, and indivisibility
Mengshi Li +3 more
doaj +1 more source
Data-Efficient Hierarchical Reinforcement Learning
, 2018 Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios.
Nachum, O., Gu, S., Lee, H., Levine, S.
openaire +3 more sources
Energy Consumption Optimization in Trajectory Planning for Fuel Cell Hybrid Uavs Based On HMPC
Advanced Robotics Research, EarlyView.The endurance limitation of multirotor drones is a critical challenge. This study adopts a hybrid power system of fuel cells and lithium‐ion batteries. Using Nondominated Sorting Genetic Algorithm II, it integrates trajectory planning with energy management optimization.
Xindi Wang +7 more
wiley +1 more source
Multi‐Material Additive Manufacturing of Soft Robotic Systems: A Comprehensive Review
Advanced Robotics Research, EarlyView.This review explores the transformative role of multi‐material additive manufacturing (MMAM) in the development of soft robotic systems. It presents current techniques, materials, and design strategies that enable functionally graded and adaptive structures.
Ritik Raj +2 more
wiley +1 more source
Hard‐Magnetic Soft Millirobots in Underactuated Systems
Advanced Robotics Research, EarlyView.This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang +4 more
wiley +1 more source
Multi-Agent Hierarchical Graph Attention Actor–Critic Reinforcement Learning
EntropyMulti-agent systems often face challenges such as elevated communication demands, intricate interactions, and difficulties in transferability. To address the issues of complex information interaction and model scalability, we propose an innovative ...
Tongyue Li +5 more
doaj +1 more source