reinforcement learning algorithm
英 [ˌriːɪnˈfɔːsmənt ˈlɜːnɪŋ ˈælɡərɪðəm]
美 [ˌriːɪnˈfɔːrsmənt ˈlɜːrnɪŋ ˈælɡərɪðəm]
网络 学习算法; 强化学习算法
双语例句
- Genetic Reinforcement Learning Algorithm for Job-shop Scheduling Problem
Job-shop排序问题的遗传强化学习算法 - Reinforcement Learning Algorithm for Dynamic Policy Under Mixed Multi-agent Domains
混合多Agent环境下动态策略强化学习算法 - A Study of Hierarchical Reinforcement Learning Algorithm Based on Fuzzy Clustering
基于模糊聚类的分层强化学习方法研究 - Multi-Agent technology achieves the personalized in ITS, and reinforcement learning algorithm makes teaching strategies with the intelligent.
多代理体技术实现了教学的个性化，强化学习算法使得教学策略具有智能化。 - Through studying Markov decision-making process and reinforcement learning algorithm, have designed the shoot module based on Q learning method.
通过研究Markov决策过程与再励学习算法，设计了基于Q学习方法的射门模块。 - In this paper Q reinforcement learning algorithm is adopted for mobile robot local path planning. It makes mobile robot resolve the problem of local path planning in a complex environment.
将Q强化学习算法应用于移动机器人局部路径规划，解决了移动机器人在复杂环境中的局部路径规划问题。 - This paper extends Q-learning algorithm properly to multi-agent cooperative team domain, in which members make their decisions independently, and proposes a shared experience tuples multi-agent cooperative reinforcement learning algorithm.
针对多agent团队中各成员之间是协作关系且自主决策的学习模型，在此对Q学习算法进行了适当扩充，提出了适合于多agent团队的一种共享经验元组的多agent协同强化学习算法。 - In this paper, introducing joint-action to the traditional reinforcement learning, a new multi-agent reinforcement learning algorithm based on behavior prediction is presented and several methods for predicting other agents 'behaviors are discussed.
在传统强化学习方式中引入组合动作的基础上，本文提出了一种基于行为预测的多智能体强化学习方法，研究了对其他智能体行为进行预测的几种可行方法。 - In this paper, a hierarchical reinforcement learning algorithm is investigated for Markov Decision Process with average reward.
对平均报酬型马氏决策过程，本文研究了一种递阶增强型学习算法； - A new Multi-Agent Reinforcement Learning Algorithm and its application to multi-robot cooperation tasks
一种新的多智能体强化学习算法及其在多机器人协作任务中的应用 - A reinforcement learning algorithm based on process reward and prioritized sweeping is presented as interference solving strategy.
本文提出了基于过程奖赏和优先扫除的强化学习算法作为多机器人系统的冲突消解策略。 - Reinforcement Learning Algorithm Based on Credit of Optimal State Transition
一种基于优化状态转换信任度的增强型学习算法 - An average reward reinforcement learning algorithm for control Markov chains is presented.
讨论平均准则控制马氏链的强化学习算法。 - Based on simulated annealing and reinforcement learning algorithm, a hybrid intelligent controller was proposed to ship steering.
本文基于模拟退火-强化学习算法提出了一种混合智能控制器，应用于船舶运动航向控制中。 - A fighter safe landing lateral-directional control method is presented based on reinforcement learning algorithm ( RL), using hierarchical control of dynamical large-scale systems theory.
基于大系统递阶控制思想，提出了一种运用再励学习算法设计歼击机自动着陆横侧向协调控制系统的方法。 - This paper will study Multi-Agent Reinforcement learning method and Co-Evolutionary Algorithm on the basis of MAS. Then propose an Evolutionary Reinforcement learning algorithm for map building using multi-agent mobile robots.
本文基于MAS的理论，探讨了多Agent的再励学习方法和协同进化算法，提出了一种进化的多Agent再励学习算法，该算法应用于分散式和同质结构系统中。 - Sarsa Reinforcement Learning Algorithm Based on Neural Networks
基于神经网络的Sarsa强化学习算法 - Based on eligibility trace theory, a delayed fast reinforcement learning algorithm DFSARSA(λ) is proposed in this paper.
在对资格迹理论研究的基础上，提出了一种延迟快速强化学习算法DFSARSA（λ）（延迟快速SARSA（λ）算法）。 - Furthermore, the exploring study of the reinforcement learning algorithm adopted in the MA collaborative decision was offered.
最后，针对应用于该系统协调决策过程的强化学习算法进行了探索性研究。 - By defining the global objective of agents, a novel multiagent reinforcement learning algorithm was proposed.
为此通过定义代理协作的集体目标，提出了一种基于多代理协商的代理强化学习算法。 - This paper is concerned with the problem of a novel reinforcement learning algorithm for solving optimal average cost function.
文中利用求解最优费用函数的方法给出了一种新的激励学习算法，即基于每阶段平均费用最优的激励学习算法。 - This paper elaborates on the low learning efficiency in reinforcement learning due to improper generalization and random exploration policy under deterministic MDPS and proposes a hierarchical reinforcement learning algorithm based on system model.
针对强化学习算法的状态值泛化和随机探索策略在确定性MDP系统控制中存在着学习效率低的问题，本文提出基于模型的层次化强化学习算法。 - Then I researched how to build Chinese chess which has self-learning ability from play itself with the neural network combination the reinforcement learning algorithm, And I researched how to build Chinese chess which has self-learning ability from play itself with the database combination the reinforcement learning algorithm.
接着研究了如何把神经网络和激励学习算法结合开发自学习中国象棋的方法，并且研究了如何把激励学习与数据库结合开发自学习中国象棋的方法。 - Considering discrete and successive state space separately, a entropy based reinforcement learning algorithm and an auto-generating neural network function approximator method for reinforcement learning are researched.
分别针对离散化的状态空间和连续状态空间的压缩问题，提出了基于信息熵的强化学习算法和基于自主生成神经网络函数逼近器的强化学习算法。 - Because of the real-time characteristic of MMOG, Reinforcement Learning algorithm can not be applied well.
由于MMOG具有实时性的特点，强化学习算法不能被很好的应用。 - In order to accelerate the learning rate and have a better convergence rate, an algorithm based on cooperation learning is proposed with blackboard model, fusion algorithm and reinforcement learning algorithm unified.
为了加快学习速率，使系统能够拥有更好的收敛速度，本文将黑板模型、融合算法以及强化学习技术相结合，提出了一种基于协作学习的多用户动态频谱接入算法。 - Both of two algorithms can save computation and storage source. Thus, they can improve the efficiency of reinforcement learning. A multi-objective reinforcement learning algorithm is researched.
二者均可以起到节约存储资源和计算资源，从而提高学习效率的目的。研究了多目标强化学习方法。 - To increase the intelligence of the robot system to adapt the dynamic environment, by utilizing the self-learning, self-adaptability and memory ability of the immune system, the immunized reinforcement learning algorithm is proposed and applied to the robot system.
为了提高多机器人系统的智能性，更好的适应环境，利用免疫系统的自学习、自适应和免疫记忆特性，提出免疫强化学习方法，应用于机器人系统。 - Propose the improved Relational Reinforcement Learning algorithm through the analysis of all kinds of existing algorithms and operating mechanisms.
本文主要工作内容进行如下：1.通过分析现有各种算法及运行机制，提出了一种改进的关系强化学习算法。 - The experiments show that the combination between reinforcement learning algorithm and genetic algorithm can effectively overcome the randomness of genetic algorithm itself. And it provides a guideline of the combination between genetic algorithm and other reinforcement learning methods.
实验结果表明，强化学习中的Q-learning算法与遗传算法结合能较好地克服遗传算法自身的随机性，为遗传算法与其它强化学习算法的结合做了铺垫。