semvec/pypi-packages huggingface dataset:
easy-rl-env 0.1.0
Easily create custom RL environments for Gymnasium
Requires Python: >=3.7

# EasyRLEnv

Easily create custom RL environments with minimal boilerplate.

## Installation
```bash
pip install easy-rl-env
```

## Example
```python
from easy_rl_env import EasyEnv

class MyCustomEnv(EasyEnv):
    def __init__(self):
        super().__init__(observation_shape=(4,), action_shape=(2,), discrete=True)

    def reset(self):
        self.state = [0, 0, 0, 0]
        return self.state, {}

    def step(self, action):
        reward = 1 if action == 0 else -1
        done = False
        return self.state, reward, done, {}
```

## License
MIT
fit-growth-mcp-tools-idc 0.1.0
Add your description here
Requires Python: >=3.12

# Fit Growth MCP Tools IDC

基于MCP协议的Schema和SQL查询工具服务器。

## 功能特性

- 获取SQL数据口径信息
- 执行理财通相关的SQL查询  
- 检查服务器状态
- 获取当前系统时间

## 环境配置

1. 复制环境变量模板文件：
```bash
cp .env.example .env
```

2. 编辑`.env`文件，配置以下环境变量：

```bash
# API配置
QUERY_API_URL=http://11.168.42.59:13699/common-quiz/chain/workflow/quiz.do

# Schema查询令牌
SCHEMA_TOKEN=your_schema_token_here

# SQL执行令牌  
SQL_TOKEN=your_sql_token_here
```

## 安装依赖

```bash
uv sync
```

## 运行服务

```bash
uv run fit-growth-mcp-tools-idc
```

## 工具说明

### get_sql_schema
获取SQL数据口径信息，根据用户需求分析需要用到的数据表、字段、枚举值、关联方式和易错点等信息。

### execute_sql_query  
执行理财通相关的SQL代码，获取数据结果。

### check_server_status
检查目标服务器的连接状态。

### get_current_time
获取当前系统时间，用于处理涉及"今天"、"昨天"、"近xx天"等模糊时间描述的查询。
epsilonq 0.1.0
Lightweight RL utilities: epsilon-greedy and Q-learning update
Requires Python: >=3.6

# epsilonq

`epsilonq` is a lightweight Python package providing basic Reinforcement Learning utilities.  
It includes an **Epsilon-Greedy action selection** function and a **Q-learning update** function, making it easy to implement simple RL agents.

---

## Features
- **Epsilon-Greedy Action Selection** for balancing exploration and exploitation.
- **Q-learning Update** for updating Q-values based on experience.
- Beginner-friendly and lightweight â€” no heavy dependencies.

---

## Installation
Install from PyPI:
```bash
pip install epsilonq

# USAGE
from epsilonq import epsilon_greedy, q_learning_update

# Initialize Q-table (dictionary)
Q = {}

# Define current state and Q-values for actions
state = "A"
q_values = [0.5, 0.2, 0.9]

# Select an action using epsilon-greedy policy
action = epsilon_greedy(q_values, epsilon=0.1)

# Simulate reward and next state
reward = 1.0
next_state = "B"

# Perform Q-learning update
q_learning_update(Q, state, action, reward, next_state, alpha=0.5, gamma=0.9)

print("Updated Q-table:", Q)


---

## **Step 4 â€” Create `setup.py`**
This file tells PyPI how to build your package:

```python
from setuptools import setup, find_packages

setup(
    name="epsilonq",
    version="0.1.0",
    author="Your Name",
    author_email="your.email@example.com",
    description="Lightweight RL utilities: epsilon-greedy and Q-learning update",
    long_description=open("README.md").read(),
    long_description_content_type="text/markdown",
    url="https://pypi.org/project/epsilonq/",
    packages=find_packages(),
    install_requires=[],
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: MIT License",
        "Operating System :: OS Independent",
    ],
    python_requires=">=3.6",
)