搜索资源列表
SQLiteBenchmark
- SQLite Benchmark是学习Android反编译时，模仿RL Benchmark做的一个测试安卓数据库性能的APP，源码仅供学习交流之用，请勿用于商业用途。-SQLite Benchmark is a Android decompile study app,original app is RL Benchmark. Use only for exchange of learning, please do not use for commercial purposes.
CW
- It s a cliff walking code using RL in MATLAB
mtncarMatlab.tar
- mountain car algorithm using RL
rl
- 简单的ASP日历范例，用了3个页面和一个ACCESS数据库 Tags： ASP日历范例-A simple example of the ASP calendar, with 3 pages and a ACCESS Tags: ASP calendar example
MQAM-RL-BER
- MQAM在瑞利信道下的分集接收最大比合并的误码性能对比-MQAM receiver error performance than the combined maximum contrast diversity under Rayleigh channel
raydriven
- 工业CT平行束到扇形束算法转换以及R-L滤波的MATLAB实现-Industrial fan beam to parallel-beam CT algorithm conversion, and RL filter MATLAB
f
- un conrolled rectifire load rl all pol
measure
- 用于测量随机散射介质光学传输矩阵TM 直接运行主函数即可 注意相机型号：MT9F002 SLM型号：RL-SLM-R2-Used to measure the transmission matrix of random scatter medium(TM).Run the main directly camear:MT9F002 SLM:RL-SLM-R2
MDTandMU
- In SMIB system, two RL series load is connected as parallel in receiving end and the three phase circuit breaker is connected in between two RL load
singlephaseFWRls
- fullwave rectifier with rl-load simulation
singlephaseHCR2
- HALF CONTROLLED WITH rl LOAD FULLY SIMUATED
RL-An-Introduction--Python-Code
- Reinforcement Learning An Introduction一书的相关代码，实现书中案例-Reinforcement Learning An Introduction of the relevant code, to achieve the case in the book
RL overview(Policy gradient)
- review of reinforcement learning
1
- 在许多工业应用场合，安置传感器测量系统的输出是不可能或非常昂贵的。此外，物体的运动仅能在一些离散点被检测到。在这些系统中，控制目标通常不是跟踪正确的轨迹，而是在传感器的位置获得给定的状态，在这篇论文中，引入RL学习策略来控制湿式离合器，以实现快速的接合。(In many industrial applications, it is impossible or very expensive to place sensor measurement system output. In addition
2016-MLSS-RL
- pdf file about deep reinforcement learning
经典复原
- 四种经典图像复原方法对比，分别是维纳滤波，最小二乘，盲去卷积和RL(The four classical image restoration methods are Wiener filtering, least squares, blind deconvolution, and RL.)
CT成像
- 利用Radon反变化算法处理CT图像，并利用RL和SL滤波进行图像成像水平的增强。(Use inverse radon transformation to treat CT images.)
RL
- neumerical algorithm for RLC circuit solutions
TCPnet
- Configuration of RL TCPnet by user. Rev.: V4.54
1709.04326
- 多智能体设置在机器学习中的重要性日益突出。超过了最近的大量关于深度的工作多agent强化学习，层次强化学习，生成对抗网络和分散优化都可以看作是这种设置的实例。然而，多学习代理人的存在这些设置使得培训问题的非平稳常常导致不稳定的训练或不想要的最终结果。我们提出学习与对手的学习意识（萝拉），一种方法，原因的预期。其他代理的学习。罗拉学习规则包括一个额外的术语，解释了在预期的参数更新的代理政策其他药物。我们发现，利用似然比策略梯度更新的方法，可以有效地计算萝拉更新规则，使该方法适合于无模型强化学习。这