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title: Data Cleaning OpenEnv
emoji: 🧹
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
license: mit
tags:
- openenv
- data-cleaning
- reinforcement-learning
- agent
- real-world
🧹 Data Cleaning OpenEnv
An OpenEnv-compliant environment where AI agents learn to clean messy real-world datasets step by step.
🌍 Environment Description
Data cleaning is one of the most common and time-consuming tasks in real-world data workflows. Data engineers and analysts spend up to 80% of their time cleaning data before it can be used.
This environment simulates that exact challenge — an agent receives a dirty dataset and must apply a sequence of cleaning operations to match a gold standard output. Each operation provides partial reward signal, enabling reinforcement learning agents to learn incrementally.
🎯 Tasks
| Task ID | Difficulty | Description | Max Steps |
|---|---|---|---|
| easy_dedup_rename | Easy | Remove duplicates + rename columns to snake_case | 10 |
| medium_missing_dtype | Medium | Fill missing values + fix data types | 15 |
| hard_full_pipeline | Hard | Full pipeline: duplicates + missing + dtypes + outliers + schema | 20 |
| expert_sales_pipeline | Expert | Full sales pipeline with case standardization + outlier removal | 25 |
Easy Task
Agent receives an employee dataset with duplicate rows and poorly formatted column names. Must remove duplicates and rename columns to snake_case format.
Scoring: duplicate_score (0.5) + schema_score (0.5)
Medium Task
Agent receives a customer dataset with missing values in multiple columns and wrong data types. Must fill missing values using correct strategies (mean/median/mode) and fix data types.
Scoring: missing_score (0.5) + dtype_score (0.5)
Hard Task
Agent receives an orders dataset with all types of issues: duplicates, missing values, wrong types, outliers. Must run a complete cleaning pipeline in the right sequence.
Scoring: All 5 components weighted equally (0.2 each)
👁️ Observation Space
{
"task_id": "string — current task identifier",
"step": "integer — current step number",
"dataset_info": "object — summary of dataset state",
"columns": "list — column names",
"shape": "list — [rows, columns]",
"missing_values": "object — missing count per column",
"dtypes": "object — data type per column",
"duplicate_count": "integer — number of duplicate rows",
"sample_rows": "list — first 3 rows as preview",
"available_operations": "list — valid operations",
"task_description": "string — what agent must do",
"message": "string — feedback from last action"
}
⚡ Action Space
{
"operation": "one of: remove_duplicates | fill_missing | fix_dtype | remove_outliers | rename_columns | validate_schema | finish",
"parameters": {
"column": "optional — target column name",
"strategy": "optional — mean | median | mode | ffill",
"dtype": "optional — int | float | str | auto",
"method": "optional — iqr | zscore",
"mapping": "optional — column rename mapping dict"
}
}
🏆 Reward Function
Rewards are computed after every step providing dense signal:
| Component | Description |
|---|---|
| duplicate_score | How close row count is to gold standard |
| missing_score | Proportion of missing values filled correctly |
| dtype_score | Proportion of columns with correct data types |
| outlier_score | How close numeric distributions are to gold |
| schema_score | Proportion of column names matching gold |
| penalty | Small penalty for using too many steps |
Total reward = weighted sum of components (0.0 to 1.0)
🚀 Setup & Usage
Run with Docker
docker build -t data-cleaning-openenv .
docker run -p 7860:7860 data-cleaning-openenv
API Usage
import requests
# Reset environment
response = requests.post(
"http://localhost:7860/reset/easy_dedup_rename"
)
obs = response.json()
# Take action
action = {
"operation": "remove_duplicates",
"parameters": {}
}
response = requests.post(
"http://localhost:7860/step/easy_dedup_rename",
json=action
)
result = response.json()
print(result["reward"]["total"])
# Get state
state = requests.get(
"http://localhost:7860/state/easy_dedup_rename"
).json()
Run Baseline Inference
export HF_TOKEN=your_token_here
export MODEL_NAME=meta-llama/Llama-3.3-70B-Instruct
export API_BASE_URL=https://router.huggingface.co/v1
python inference.py
📊 Baseline Scores
Scores produced by meta-llama/Llama-3.3-70B-Instruct:
| Task | Score |
|---|---|
| easy_dedup_rename | 1.0000 |
| medium_missing_dtype | 1.0000 |
| hard_full_pipeline | 1.0000 |
| expert_sales_pipeline | 1.0000 |
| Average | 1.0000 |
📁 Project Structure
data-cleaning-openenv/
├── main.py # FastAPI server
├── environment.py # Core env logic
├── models.py # Pydantic models
├── inference.py # Baseline script
├── openenv.yaml # OpenEnv metadata
├── Dockerfile # Container config
├── README.md # This file
└── datasets/
├── task_metadata.json
├── easy/
│ ├── dirty.csv
│ └── gold.csv
├── medium/
│ ├── dirty.csv
│ └── gold.csv
└── hard/
├── dirty.csv
└── gold.csv