搜索资源列表
MobileRobotSimQ
- 使用Q学习的一种强化学习算法，针对路径规划问题，用Q学习的方法解决-A method to solve planning the path, using Q_study, one method of reforence study.
matlab-QLEARNING
- 模拟机器人路径规划,采用强化学习中的Q学习算法来实现,最后会返回机器人选择路径的坐标位置-code for path searching
Q-Learning-Example-1
- Q-学习是一种重要的强化学习方法，提供一个Q-学习做路径规划的例子，初学者可以通过代码学习Q-学习的原理。-Q- learning is an important reinforcement learning methods, to provide an example of Q- learning to do path planning, beginners can learn the principles of Q- code.
ReinforcementLearning
- 用强化学习方法解决简单的走迷宫问题，得到智能体的最优路径(Using reinforcement learning to solve simple maze problem)
code
- Q-learning 算法实现AGV的最优路径规划，实测效果非常好，对于研究深度学习和强化学习的同学很有帮助！(The Q-learning algorithm realizes the optimal path planning of AGV, and the measured results are very good. It is very helpful for students who are studying deep learning and reinforcement learn
单一任务导航
- 测试深度马尔可夫决策来导航,给出了python的实现代码(MDP based navigation)
reinforcement-learning-master
- 在障碍物环境下的基于强化学习的单智能体与多智能体路径规划算法(Single agent and multi-agent path planning algorithm based on reinforcement learning in obstacle environment)