Results 41 to 50 of about 2,703 (160)
Rare Metals, Volume 45, Issue 1, January 2026.ABSTRACT High‐entropy alloys (HEAs) have emerged as a transformative class of materials distinguished by their complex chemical compositions, unique microstructures, and remarkable mechanical and functional properties. Traditionally, the discovery and optimization of HEAs have relied on conventional methods, including trial‐and‐error experimentation ...
Chrispin Ouko Zamzu +2 more
wiley +1 more source
Recursive deep reinforcement learning-based collaborative caching relay algorithm in mobile vehicular edge network [PDF]
Considering scenarios without road side unit coverage, a recursive deep reinforcement learning-based collaborative caching relay algorithm was proposed to construct a caching system by leveraging the cooperation among vehicles.
MA Huahong +3 more
core +1 more source
Off-policy Maximum Entropy Deep Reinforcement Learning Algorithm Based on RandomlyWeighted Triple Q -Learning [PDF]
, 2022 Reinforcement learning is an important branch of machine learning.With the development of deep learning,deep reinforcement learning research has gradually developed into the focus of reinforcement learning research.Model-free off-policy deep ...
FAN Jing-yu, LIU Quan
core +1 more source
International Journal of Urban and Regional Research, Volume 50, Issue 1, Page 191-220, January 2026.Abstract Henry George advocated for capturing land value increases for public ends. The active approach of public authorities organizing and financing land development can help capture higher land value increases, as Hartman and Spit indicate. However, this approach hardly happens in developing countries, where the coalition of private developers and ...
Nannan Xu
wiley +1 more source
Research progress of causal inference in reinforcement learning framework(强化学习框架中因果推断研究进展)
Zhejiang Daxue xuebao. Lixue banCausal reasoning has been extensively studied in all fields of science. In recent decades, there have been a number of innovations in the development and implementation of methods aimed at determining causality.
刘华玲(LIU Hualing) +2 more
doaj +1 more source
大数据新闻推荐系统对新媒体新闻传播有着重要作用。提出了一种以深度强化学习为基础的推荐系统，旨在结合神经网络的表征能力和强化学习的策略选择能力来提升新闻推荐效果。使用动态动作掩码加强对用户短期兴趣的判断能力，使用优化缓存机制提升经验缓存的使用效率，通过区域遮蔽性质的奖励设计加快模型训练，从而提高推荐系统在新闻推荐领域的表现。实验表明，所提模型在新闻数据集上的推荐准确率与主流的神经网络推荐方法相当，且在排序性能上优于当前先进的推荐算法。
董相宏, 安俊秀
doaj +1 more source
智能科学与技术学报, 2022 当代系统认知、管理与控制的核心理论、方法与技术已经转移到大数据和人工智能技术上，这导致当前人工智能技术条件局限与复杂系统认知、管理、控制的需求之间形成了一道鸿沟。因此，现实的需求催生了人工智能的一种新型形态——人机混合增强智能形态，即人类智能与机器智能协同贯穿于系统认知、管理、控制等过程的始终，人类的认知和机器智能认知互相混合，形成增强型的智能形态，这种形态是人工智能或机器智能可行的、重要的成长模式。提出了一种物理-数据-知识混合驱动的人机混合增强智能系统管控方法。从可信分布式数据、计算和算法 ...
张俊 +12 more
doaj
Intelligent anti-jamming decision algorithm based on proximal policy optimization [PDF]
The existing intelligent anti-jamming methods based on deep reinforcement learning are applied to space-ground TT&C and communication links, in which the deep neural network used for decision-making has a complex structure, and the resources of ...
HUANG Wei +4 more
core +1 more source
Large-scale post-disaster user distributed coverage optimization based on multi-agent reinforcement learning [PDF]
, 2022 In order to quickly restore emergency communication services for large-scale post-disaster users, a distributed intellicise coverage optimization architecture based on multi-agent reinforcement learning (RL) was proposed, which could address the ...
Fengyu WANG +5 more
core +1 more source
Policy &Internet, Volume 17, Issue 3, September 2025.ABSTRACT Moderating harmful and offensive content on social media is challenging for digital platforms that seek to balance regulation and censorship across a diverse user group. It is further complicated by discrepancies between platform policies, user expectations and user experiences.
Asher Flynn +4 more
wiley +1 more source