Results 141 to 150 of about 6,930,474 (290)

Confidence Aware Inverse Constrained Reinforcement Learning

CoRR
In coming up with solutions to real-world problems, humans implicitly adhere to constraints that are too numerous and complex to be specified completely. However, reinforcement learning (RL) agents need these constraints to learn the correct optimal policy in these settings.
Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart   +4 more
openaire   +4 more sources

Research on multi-objective tourism path planning and resource allocation based on graph neural networks and deep reinforcement learning

Discover Artificial Intelligence
Based on graph neural networks and reinforcement learning, this study designs three algorithms for multi-objective tourism route planning under different constraints: unconstrained, capacity-constrained, and demand-splitting scenarios.
YinChao Ma
doaj   +1 more source

ISCC: Intelligent Semantic Caching and Control for NDN-Enabled Industrial IoT Networks

IEEE Access
Effective content caching plays a crucial role in meeting the latency and reliability requirements of Industrial Internet of Things (IIoT) networks. However, existing caching mechanisms in NDN-based IIoT networks often exhibit problems in adaptability ...
Zupash Awais, Muzammil Hussain, Ayman Elshenawy, Ahmad Arsalan, Muhammad Anwar, Muhammad Asif Habib, Sohail Jabbar, Mudassar Ahmad   +7 more
doaj   +1 more source

Advanced Manufacturing of Composite‐Based Systems for Energy Applications

Advanced Materials Technologies, EarlyView.
Advanced manufacturing enables the integration of polymers, ceramics, metal oxides, and composites into architected microstructures with tailored transport pathways. By coupling material selection, manufacturing strategy, and structural design, multifunctional energy systems can simultaneously improve electrochemical performance, thermal management ...
Sri Vaishnavi Thummalapalli, Dhanush Patil, Libin Yang, M. Taylor Sobczak, Anshul Kulkarni, Xiao Sun, Arunachalam Ramanathan, Tiantian Li, Ramaraja P. Ramasamy, Jason J. Locklin, Yanyu Chen, Xianqiao Wang, Arunachala Mada Kannan, Kenan Song   +13 more
wiley   +1 more source

Predictive Lagrangian Optimization for Constrained Reinforcement Learning

2025 IEEE 64th Conference on Decision and Control (CDC)
Constrained optimization is popularly seen in reinforcement learning for addressing complex control tasks. From the perspective of dynamic system, iteratively solving a constrained optimization problem can be framed as the temporal evolution of a feedback control system.
Tianqi Zhang, Puzhen Yuan, Guojian Zhan, Ziyu Lin, Yao Lyu, Zhenzhi Qin, Jingliang Duan, Liping Zhang 0008, Shengbo Eben Li   +8 more
openaire   +3 more sources

Programmable Pneumatic Actuator System for a Bioinspired Artificial Colon

Advanced Materials Technologies, EarlyView.
A modular soft robotic colon simulator driven by programmable pneumatic actuation is developed to mimic physiological and pathological‐like motility patterns of the large intestine. Finite element–guided design and distributed control enable anatomically realistic deformation for peristalsis, segmentation, and mass movements, supporting capsule ...
Andrew Bickerdike, Bo Tian, Xizheng Fang, Zepeng Wang, Yang Liu, Lucy Adams, Shyam Prasad   +6 more
wiley   +1 more source

Dog Behaviour - Effect of Delay To Reinforcement [PDF]

, 2012
Dogs were fed dry food as reinforcement and were required to touch a wand with their nose to get that reinforcement. In the first study, half the dogs were given immediate reinforcement, while for the remaining dogs reinforcement was delayed by two ...
Lord, Sarah
core  

A Survey of Risk-Calibrated Certifiably Safe and Resource-Aware (RCSR) Path Planning for Unmanned Aerial Vehicles

Drones
Effective mission planning, path search, and path following are critical for unmanned aerial vehicles (UAVs) operating in complex, dynamic, and resource-constrained environments.
Nathan Johnson, Sima Shafaei, Andrew Karem, Sayani Sarkar   +3 more
doaj   +1 more source

On‐Chip Photonic Neural Network Architectures

Advanced Optical Materials, EarlyView.
This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong, Berkay Neseli, Seungyeon Cho, Seungsoo Lee, Yeonjun Kim, Seungyoon Choi, Hyo‐Hoon Park, Hamza Kurt   +7 more
wiley   +1 more source

Knowledge and Ignorance in Reinforcement Learning [PDF]

, 2011
The field of Reinforcement Learning is concerned with teaching agents to take optimal decisions to maximize their total utility in complicated environments. A Reinforcement Learning problem, generally described by the Markov Decision Process formalism,
Ramachandran, Deepak
core