semvec/pypi-packages huggingface dataset:
maji-cli 0.0.1
Something you wouldn't recognise. It's called love.
License: MIT
Requires Python: >=3.7, <4
Keywords: io,load,dump,read,write,parse,format,utils

[![CircleCI](https://circleci.com/gh/ylathouris/maji.svg?style=shield)](https://circleci.com/gh/ylathouris/maji)  ![Coverage](coverage.svg)

---

# Maji Command Line Interface (CLI)

To be continued...
maji 0.0.1
Something you wouldn't recognise. It's called love.
License: MIT
Requires Python: >=3.7, <4
Keywords: io,load,dump,read,write,parse,format,utils

[![CircleCI](https://circleci.com/gh/ylathouris/maji.svg?style=shield)](https://circleci.com/gh/ylathouris/maji)  ![Coverage](coverage.svg)

---

# Maji

To be continued...
mPython-motor 0.0.3
Motor control by i2c communication for mPython library
License: MIT

<img width="400" src="/images/mpython.png"/>

## 概述

Motor control by i2c communication for mPython library

更多的mPython library：https://github.com/labplus-cn/mPython-lib

## 库的安装方法

可通过以下任一方法进行安装。
1. 将项目中的`motor.py` 
2. 在掌控板REPL界面中，使用upip安装，步骤如下：
    * 前置条件需要掌控板连接网络
    * 导入upip模块，执行`import upip`
    * 执行`upip.install('mPython-motor'）

```python
>>> import upip
>>> upip.install('mPython-motor')
```

## 使用

电机控制通讯协议数据：

| Type    | Command  | motor_no  | speed(int) |
| :------ | :------  | :------   | :------    |
| 控制电机 | 0x01     | 0x01/0x02 | -100~100   |

</br>
motor Library Documentation：https://mpython-lib.readthedocs.io

## 执照

所有代码均在MIT许可下发布。
RLDQN 0.0.6
Asynchronous DQN
License: MIT

$RLDQN
========

An asynchronous deep Q-learning Network for training Game AI.


Look how easy it is to use:
------------------------------

    import dqn

    # Runing our model to train you own AI(The model will be saved in dictionary 'logs')
    
    dqn.run_dqn.run('Game Name')
    
    # Evaluating the effect of the model
    
    dqn.run_dqn.evaluate
    


You can see demos through download files.(Source file)

We provide several demos to show how to use these module in games.

And you can also see source code and demos on github.


Features
--------

- You do not need to know the algorithm.
- Make things faster
- Use the DQN to train games of yourself.


Installation
------------

Install RLDQN by running:

    pip install RLDQN

 
Contribute
----------

- Source Code: 

Support
-------

If you are having issues, please let us know.

We have a mailing list located at: 396032050@qq.com