Results 41 to 50 of about 4,328 (167)
智能科学与技术学报, 2020 多智能体系统因其在工程、社会科学和自然科学等多学科领域具有潜在、广泛的应用性，在过去的 20 年里引起了研究者的广泛关注。实现多智能体系统的一致性通常需要求解相关矩阵方程离线设计控制协议，这要求系统模型精确已知。然而，实际上多智能体系统具有大规模尺度、非线性耦合性特征，并且环境动态变化，使得系统精确建模非常困难，这给模型依赖的多智能体一致性控制协议设计带来了挑战。强化学习技术因其可以利用沿系统轨迹的测量数据实时学习控制问题的最优解，被广泛用于解决复杂系统最优控制和决策问题。综述了利用强化学习技术 ...
李金娜, 程薇燃
doaj
Deep reinforcement learning based resource allocation algorithm in cellular networks [PDF]
, 2019 In order to solve multi-objective optimization problem,a resource allocation algorithm based on deep reinforcement learning in cellular networks was proposed.Firstly,deep neural network (DNN) was built to optimize the transmission rate of cellular system
Jia SHI +5 more
core +1 more source
Learner‐Focused Strategy Instruction From the Teachers’ and the Learners’ Perspective
International Journal of Applied Linguistics, EarlyView.ABSTRACT Numerous large‐scale quantitative studies have been conducted to yield a macro‐level picture of the effectiveness of strategy instruction. However, little is understood about learners' actual processing of strategy instruction and interaction with the teachers delivering it.
Isobel Kai‐Hui Wang, Andrew D. Cohen
wiley +1 more source
智能科学与技术学报, 2022 随着基础理论和硬件计算能力的飞速发展，深度学习技术在众多领域取得了令人瞩目的成绩。作为描述客观物理世界的重要工具，长期以来微分方程是各领域研究人员关心的重点。近年来，深度学习和微分方程的结合逐渐成了研究的热点。由于深度学习能够从大量数据中高效地提取特征，微分方程能够反应客观的物理规律，因此二者的结合可以有效地提升深度学习的泛化性，同时增强深度学习的可解释性。首先，介绍了深度学习求解微分方程的基本问题。其次，介绍了两类深度学习求解微分方程的方法：数据驱动和物理知情方法。然后 ...
卢经纬, 程相, 王飞跃
doaj
A transfer reinforcement learning-based approach for cross-domain charging station recommendation in the Internet of vehicles [PDF]
Deep reinforcement learning has been widely applied in charging station recommendations in the internet of vehicles, but training separate neural networks for each region are often required by traditional methods, leading to increased computational load ...
CAO Yue +4 more
core +1 more source
Learning Styles, Engagement and Anxiety in AI‐Mediated Writing: A Multimodal Feedback Study
International Journal of Applied Linguistics, EarlyView.ABSTRACT Artificial intelligence (AI) tools now permeate English academic writing. However, evidence on how feedback modalities align with student differences and with psychological mechanisms remains limited. Prior work often reduced learning styles to simple matches with delivery modes and treated learning engagement and writing anxiety as peripheral.
Yi Ren +3 more
wiley +1 more source
Gradient descent Sarsa(?)algorithm based on the adaptive potential function shaping reward mechanism [PDF]
, 2013 In the reinforcement leaning tasks with continuous state spaces,the algorithms are usually facing the problems of ill initial performance and low convergence speed.In order to solve these problems,the potential function shaping reward mechanism was ...
Fei XIAO +4 more
core
International Journal of Applied Linguistics, EarlyView.ABSTRACT Generative artificial intelligence (GenAI) has emerged as a powerful tool in the Informal Digital Learning of English (IDLE) environment, offering personalized, interactive, and innovative language learning experiences that may enhance learning engagement.
Jin Liu, Meilu Liu, Yuan Yao, Dechao Li
wiley +1 more source
Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy [PDF]
For the deep reinforcement learning (DRL)-empowered intelligent jamming, an anti-jamming strategy aided by sample information entropy was proposed. Firstly, the anti-jamming strategy network and entropy prediction network were designed based on neural ...
CHEN Qianbin +6 more
core +1 more source
Optimization mechanism of attack and defense strategy in honeypot game with evidence for deception [PDF]
, 2022 Using game theory to optimize honeypot behavior is an important method in improving defender’s trapping ability.Existing work tends to use over simplified action spaces and consider isolated game stages.A game model named HoneyED with expanded action ...
Changyou XING +3 more
core +1 more source