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| name: dataclean-env | |
| version: 2.0.0 | |
| description: > | |
| Real-world data cleaning and ETL environment for AI agent RL training. | |
| 4 tasks including the novel Data Drift task — live streaming rows injected | |
| mid-episode every 5 steps, simulating a Kafka pipeline under continuous drift. | |
| author: Team DataClean | |
| tags: | |
| - openenv | |
| - data-cleaning | |
| - etl | |
| - real-world | |
| - tabular | |
| - data-drift | |
| - streaming | |
| tasks: | |
| - id: task1 | |
| name: Null Fixer | |
| difficulty: easy | |
| max_steps: 10 | |
| description: Fix null values and wrong dtypes in a 50-row customer dataset. | |
| - id: task2 | |
| name: Schema Normalizer | |
| difficulty: medium | |
| max_steps: 20 | |
| description: Deduplicate, normalize country names, fix date format, fill nulls. | |
| - id: task3 | |
| name: ETL Pipeline | |
| difficulty: hard | |
| max_steps: 30 | |
| description: Merge two tables, remove outliers, add derived column. | |
| - id: task4_data_drift | |
| name: Data Drift (Streaming) | |
| difficulty: expert | |
| max_steps: 40 | |
| description: > | |
| NOVEL. Live streaming transactions — fresh dirty rows injected every 5 steps | |
| mid-episode. Agent must clean continuously under real-time data drift. | |
| environment: | |
| type: http | |
| port: 7860 | |
| endpoints: | |
| reset: POST /reset | |
| step: POST /step | |
| state: GET /state | |
| tasks: GET /tasks | |
| grader: GET /grader | |
| baseline: GET /baseline | |
| health: GET /health |