- reinforcement learning algorithm
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强化学习算法的英文
例句与用法
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- Firstly , we have put forward a mathematic form ? interest vector - to express the user ' s interest and have given a formula to calculate quantificationally the interest degree of interest item . secondly , we apply the method of reinforcement learning to intelligent learning agent to make it leam the user ' s interest more precision , more quickly and more efficiency , to make it discover the divert of the user ' s interest in time . thirdly , we have developed a mean of information search and filtrate on user ' s interest
这些研究工作主要体现在：提出用户兴趣的数学表示形式? ?兴趣向量，并提出定量地计算用户对兴趣项的兴趣度的计算方法；提出采用强化学习算法，使智能学习agent能够更准确，更快速和更高效地学习到用户的兴趣和及时发现用户兴趣的转移；提出面向用户兴趣的信息搜索和信息过滤方法；提出一种基于对象的agent编程模式，也称为用扩充oo建模技术或方法学的适用性来设计agent系统。 - The aco algorithms are fitted into the framework of generalized policy iteration ( gpi ) in rl based on incomplete information of the markov state . furthermore , we show that the pheromone update in the acs and ant - q algorithm is based on the mc methods or some formalistic combination of mc methods and td methods
此外在强化学习的理论框架内说明了as算法是一种基于蒙特卡洛方法的强化学习算法， acs和ant - q算法是一种蒙特卡洛方法与瞬时差分方法在形式上相结合的强化学习算法。 - Multi - agent cooperation based on reinforcement learning is studied in this dissertation . macm provides a flexible mechanism to support learning in multi - agent systems and our new algorithm not only can reduce the store space of q - table but also can coverge to optimal equilibrium rapidly
本章研究基于强化学习的多agent系统协作机制，提出一种新的协作模型macm ，该模型通过提供灵活摘要协调机制支持多agent之间的协作及协作过程中的学习，并针对macm模型提出了一种改进的分布式强化学习算法。 - In the multi - agents systems about manufacturing , whether an agent can adapt in the undefined environment and learn from the background knowledge is essential . to improve the adaptation of multi - agent system , we can strengthen the ability of learning from the environment . this paper analyzes the reinforcement learning in the field of artificial intelligent and puts forward improved reinforcement learning arithmetic that combines reinforcement learning with background knowledge , which can speed the reinforcement learning and satisfy the demand of real time
在多代理系统中，代理要适应周围环境的变化并对环境的变化做出反应，自学习能力是很重要的因素之一，本论文在分析强化学习（ reinforcementlearning ）的基础上，提出了改进的强化学习算法，即将强化学习和历史经验相结合，既能提高学习的效率和收敛速度，又能满足实时性的要求。