Research on local path planning of mobile robot based on Q reinforcement learning and CMAC neural networks.
基于Q强化学习与CMAC神经网络的移动机器人局部路径规划研究。
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 sample graph is from a simple reinforcement learning application that USES Q learning.
这个示例图是从使用Q学习的一个简单增强式学习应用程序中得到的。
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。
Q learning algorithm is the most popular reinforcement learning algorithm, but the algorithm exist some problems.
目前主流的强化学习算法是Q学习算法，但Q学习本身存在一些问题。
For reinforcement learning control in continuous Spaces, a Q-learning method based on a self-organizing fuzzy RBF (radial basis function) network is proposed.
针对连续空间下的强化学习控制问题，提出了一种基于自组织模糊rbf网络的Q学习方法。
The reinforcement learning is adopted to control and decision for AUV, and Q-learning, BP neural net, artificial potential is integrated to avoidance planning for AUV.
主要采用强化学习的方法对AUV进行控制和决策，综合Q学习算法、BP神经网络和人工势场法对AUV进行避碰规划。
Q-learning is a typical Reinforcement Learning (RL) method with a slow convergence speed especially as the scales of the state space and action space increase.
学习是一种典型的强化学习，其学习效率较低，尤其是当状态空间和决策空间较大时。
Q-learning is a typical Reinforcement Learning (RL) method with a slow convergence speed especially as the scales of the state space and action space increase.
学习是一种典型的强化学习，其学习效率较低，尤其是当状态空间和决策空间较大时。
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