maxim

下载插件:
https://github.com/zhangzhao4444/Maxim

进入monkey.jar 和 framework.jar 的目录下执行:

adb push framework.jar /sdcard
adb push monkey.jar /sdcard

不要忘记这一步哦~~push 到手机到 sdcard 中

adb shell CLASSPATH=/sdcard/monkey.jar:/sdcard/framework.jar exec app_process /system/bin tv.panda.test.monkey.Monkey -p com.xueqiu.android --uiautomatormix --running-minutes 60 -v -v

this file contains:Advanced Deep Learning with Keras_ Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more (2018, Packt Publishing.pdf Deep Reinforcement Learning for Wireless Networks (2019, Springer International Publishing).pdf Deep Reinforcement Learning Hands-On_ Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more.pdf Hands-On Reinforcement Learning with Python_ Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow (2018, Packt Publishing).epub Hands-On Reinforcement Learning with Python_ Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow (2018, Packt Publishing).pdf Keras Reinforcement Learning Projects (2018, Packt Publishing).epub Keras Reinforcement Learning Projects (2018, Packt Publishing).pdf Practical Reinforcement Learning Develop self-evolving, intelligent agents with OpenAI Gym, Python and Java.pdf Python Reinforcement Learning Projects - 2018.pdf Reinforcement Learning for Optimal Feedback Control (2018, Springer International Publishing).pdf Reinforcement Learning with TensorFlow_ A beginner’s guide to designing self-learning systems with TensorFlow and OpenAI Gym (2018, Packt Publishing).pdf Reinforcement Learning _ With Open AI, TensorFlow and Keras Using Python-Apress (2018).pdf Reinforcement Learning_ An Introduction (2018, The MIT Press).pdf Simulation-Based Optimization_ Parametric Optimization Techniques and Reinforcement Learning (2015, Springer US).pdf Statistics for Machine Learning_ Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R-Packt Publishing (2017).pdf Tensorflow for Deep Learning_ From Linear Regression to Reinforcement Learning (2018, O'Reilly Media).pdf
The topic of this book is Reinforcement Learning—which is a subfield of Machine Learning—focusing on the general and challenging problem of learning optimal behavior in complex environment. The learning process is driven only by reward value and observations obtained from the environment. This model is very general and can be applied to many practical situations from playing games to optimizing complex manufacture processes. Due to flexibility and generality, the field of Reinforcement Learning is developing very quickly and attracts lots of attention both from researchers trying to improve existing or create new methods, as well as from practitioners interested in solving their problems in the most efficient way. This book was written as an attempt to fill the obvious lack of practical and structured information about Reinforcement Learning methods and approaches. On one hand, there are lots of research activity all around the world, new research papers are being published almost every day, and a large portion of Deep Learning conferences such as NIPS or ICLR is dedicated to RL methods. There are several large research groups focusing on RL methods application in Robotics, Medicine, multi-agent systems, and others. The information about the recent research is widely available, but is too specialized and abstract to be understandable without serious efforts. Even worse is the situation with the practical aspect of RL application, as it is not always obvious how to make a step from the abstract method described in the mathematical-heavy form in a research paper to a working implementation solving actual problem. This makes it hard for somebody interested in the field to get an intuitive understanding of methods and ideas behind papers and conference talks. There are some very good blog posts about various RL aspects illustrated with working examples,
### Maxim SDK 的教学资料与使用指南 Maxim SDK 是一种用于嵌入式设备开发的软件工具包,通常支持多种硬件平台并提供丰富的功能模块。以下是有关 Maxim SDK 的教程、使用指南和示例代码的相关信息: #### 1. 官方文档与资源 官方文档通常是学习任何 SDK 的最佳起点。Maxim Integrated 提供了详细的开发者手册和技术参考资料,这些材料涵盖了从基础配置到高级应用的所有方面[^4]。 - **安装与配置**: 开发者可以访问 Maxim 官网下载最新的 SDK 版本,并按照《快速入门指南》完成环境搭建。 - **API 参考**: API 文档提供了函数原型、参数说明以及返回值解释,帮助开发者理解各个组件的功能[^5]。 #### 2. 示例代码分析 为了加速项目开发进程,许多厂商会随 SDK 发布一些典型的例子程序。对于 Maxim SDK 而言,其示例代码主要集中在以下几个领域: - **GPIO 控制** ```c void configure_gpio(int pin, int direction) { // 假设方向为0表示输入模式,1表示输出模式 if (direction == 0 || direction == 1){ MAXIM_GPIO_CONFIG(pin, direction); } } ``` - **I²C 驱动** ```c uint8_t read_i2c_register(uint8_t device_addr, uint8_t reg_addr) { uint8_t data; maxim_i2c_start(); maxim_i2c_write(device_addr << 1 | 0); maxim_i2c_write(reg_addr); maxim_i2c_stop(); maxim_i2c_start(); maxim_i2c_write(device_addr << 1 | 1); data = maxim_i2c_read(MAXIM_I2C_NACK); maxim_i2c_stop(); return data; } ``` 上述片段展示了如何通过 I²C 协议读取寄存器数据[^6]。 #### 3. 社区支持与其他资源 除了官方渠道外,在线社区也是获取技术支持的重要途径之一。Stack Overflow 和 GitHub 等平台上存在大量由其他开发者分享的经验贴子及开源项目[^7]。 ---
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