This paper discusses reinforcement learning(RL)algorithm and its application to technical action learning of soccer robot.
主要研究了强化学习算法及其在机器人足球比赛技术动作学习问题中的应用。
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。
By adopting the value iterative strategies of reinforcement learning, the algorithm can absorb the corresponding knowledge from its environment during its running and improve its search ability.
该算法采用强化学习中值迭代策略，在运行中能够从环境中获取相应知识，提高其搜索能力。
The paper proposes a model of reinforcement learning based on ant colony algorithm, namely the combination of ant colony algorithm and Q learning.
本文提出了一种基于蚁群算法的强化学习模型，即蚁群算法与Q学习相结合的思想。
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。
A network spider algorithm based on the reinforcement learning is proposed and deployed to discovery the web site of dinning.
提出了基于强化学习的网络爬虫算法，并应用于餐饮类站点的发现中。
And through compare with non-Evolutionary Map building method, the Evolutionary Reinforcement learning algorithm can increase search map efficiency and expedite convergence speed of global map.
通过与非进化模式下的多机器人地图构建方法的比较，该算法可以提高地图搜索的效率，加快全局地图的收敛。
And through compare with non-Evolutionary Map building method, the Evolutionary Reinforcement learning algorithm can increase search map efficiency and expedite convergence speed of global map.
通过与非进化模式下的多机器人地图构建方法的比较，该算法可以提高地图搜索的效率，加快全局地图的收敛。
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