README.md

metadata

title: Data Cleaning OpenEnv
emoji: 🧹
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false

🧹 Data Cleaning OpenEnv

A complete OpenEnv-compatible reinforcement learning environment for data cleaning tasks.

Team: Soham Sandeep Kamathi, Manas Mahendra Patil, Shivam Jha Hackathon: Meta x PyTorch OpenEnv Hackathon 2026

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What This Environment Does

An AI agent receives messy tabular datasets and must apply the correct cleaning operations to earn rewards. Three tasks of increasing difficulty simulate real-world data quality problems.

Tasks

Task	Difficulty	What the agent must do	Reward
remove_nulls	Easy	Drop rows containing null/missing values	0.0–1.0
fix_dates	Medium	Standardise inconsistent date formats to YYYY-MM-DD	0.0–1.0
remove_outliers	Hard	Remove statistical outliers via IQR method from salary and age columns	0.0–1.0

Observation Space

Each observation returns a DatasetObservation with:

• dataset_preview — first 5 rows as string
• null_count — number of missing values
• date_format_errors — number of non-standard dates
• outlier_count — number of outliers detected
• task_description — plain-English task description
• hint — suggested action

Action Space

A CleaningAction with:

• task_id — 1, 2, or 3
• action_type — one of remove_nulls, fix_dates, remove_outliers
• column — optional column name (e.g. hire_date, salary, all)

API Endpoints

Method	Path	Description
POST	/reset?task_id=1	Start new episode
POST	/step	Submit cleaning action
GET	/state?task_id=1	Get session metadata
GET	/tasks	List all tasks
GET	/health	Health check

Setup & Run

# Install dependencies
pip install -r requirements.txt
# Run locally
uvicorn app:app --host 0.0.0.0 --port 7860