A review of reinforcement learning based intelligent optimization for manufacturing scheduling
As the critical component of manufacturing systems, production scheduling aims to optimize
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
Reinforcement learning in sustainable energy and electric systems: A survey
The dynamic nature of sustainable energy and electric systems can vary significantly along
with the environment and load change, and they represent the features of multivariate, high …
with the environment and load change, and they represent the features of multivariate, high …
Deterministic policy gradient algorithms
In this paper we consider deterministic policy gradient algorithms for reinforcement learning
with continuous actions. The deterministic policy gradient has a particularly appealing form …
with continuous actions. The deterministic policy gradient has a particularly appealing form …
In-network machine learning using programmable network devices: A survey
Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …
classification and anomaly detection to network configuration. However, machine learning …
深度强化学习算法与应用研究现状综述
刘朝阳， 穆朝絮， 孙长银 - 智能科学与技术学报, 2020 - infocomm-journal.com
深度强化学习主要被用来处理感知− 决策问题, 已经成为人工智能领域重要的研究分支.
概述了基于值函数和策略梯度的两类深度强化学习算法, 详细阐述了深度Q 网络 …
概述了基于值函数和策略梯度的两类深度强化学习算法, 详细阐述了深度Q 网络 …
[PDF][PDF] 融合LSTM 和PPO 算法的移动机器人视觉导航
张仪， 冯伟， 王卫军， 杨之乐， 张艳辉… - 电子测量与仪器 …, 2023 - jemi.cnjournals.com
为提高移动机器人在无地图情况下的视觉导航能力, 提升导航成功率, 提出了一种融合长短期
记忆神经网络(long short term memory, LSTM) 和近端策略优化算法(proximal policy …
记忆神经网络(long short term memory, LSTM) 和近端策略优化算法(proximal policy …
DM-DQN: Dueling Munchausen deep Q network for robot path planning
Y Gu, Z Zhu, J Lv, L Shi, Z Hou, S Xu - Complex & Intelligent Systems, 2023 - Springer
In order to achieve collision-free path planning in complex environment, Munchausen deep
Q-learning network (M-DQN) is applied to mobile robot to learn the best decision. On the …
Q-learning network (M-DQN) is applied to mobile robot to learn the best decision. On the …
Deep reinforcement learning-based radar network target assignment
F Meng, K Tian, C Wu - IEEE sensors journal, 2021 - ieeexplore.ieee.org
This study focuses on the problem of target assignment when a phased-array radar network
detects hypersonic-glide vehicles in near-space and proposes a method for target …
detects hypersonic-glide vehicles in near-space and proposes a method for target …
深度强化学习及在路径规划中的研究进展.
张荣霞， 武长旭， 孙同超… - Journal of Computer …, 2021 - search.ebscohost.com
路径规划的目的是让机器人在移动过程中既能避开障碍物, 又能快速规划出最短路径.
在分析基于强化学习的路径规划算法优缺点的基础上, 引出能够在复杂动态环境下进行良好路径 …
在分析基于强化学习的路径规划算法优缺点的基础上, 引出能够在复杂动态环境下进行良好路径 …
A deep deterministic policy gradient algorithm based on averaged state-action estimation
J Xu, H Zhang, J Qiu - Computers and Electrical Engineering, 2022 - Elsevier
Abstract Deep Reinforcement Learning (DRL), one of the most popular research topics in
artificial intelligence, has achieved a breakthrough in continuous control tasks. Nonetheless …
artificial intelligence, has achieved a breakthrough in continuous control tasks. Nonetheless …