The Task

Build a complete, real-world OpenEnv environment that an AI agent can learn from through the standard  step() / reset() / state()  API.

Key Requirements at a Glance

Must simulate a real-world task (not games or toys)

Implement full OpenEnv spec: typed models, step()/reset()/state(), openenv.yaml

Minimum 3 tasks with agent graders (easy → medium → hard, scores/reward 0.0–1.0)

Meaningful reward function with partial progress signals

Baseline inference script with reproducible scores

Deploy to Hugging Face Spaces + working Dockerfile

README with environment description, action/observation spaces, setup instructions

Functional Requirements

Real-world task simulation

The environment must simulate a task humans actually do. Not games, not toys. Examples: email triage, code review, data cleaning, scheduling, customer support, content moderation.

OpenEnv spec compliance

Implement the full OpenEnv interface: typed Observation, Action, and Reward Pydantic models. step(action) → returns observation, reward, done, info. reset() → returns initial observation. state() → returns current state. openenv.yaml with metadata. Tested via openenv validate.

Minimum 3 tasks with agent graders

Each task defines a concrete objective an agent must accomplish, with a programmatic grader that scores performance (0.0–1.0). Tasks should range: easy → medium → hard. Graders must have clear, deterministic success/failure criteria.

Meaningful reward function

Provides signal over the full trajectory (not just binary end-of-episode). Rewards partial progress toward task completion. Penalizes clearly undesirable behavior (e.g. infinite loops, destructive actions).

Baseline inference script

Uses the OpenAI API client to run a model against the environment. Reads API credentials from environment variables (OPENAI_API_KEY). Produces a reproducible baseline score on all 3 tasks.

Detailed Requirements

Non-Functional Requirements

Deploys to a Hugging Face Space

Environment must run as a containerized HF Space tagged with openenv.

Containerized execution

Must include a working Dockerfile. The environment should start cleanly with docker build + docker run.

Documentation

README must include: environment description and motivation, action and observation space definitions, task descriptions with expected difficulty, setup and usage instructions, baseline scores.






When Round 1 opens, you'll choose 1 of 4–5 problem statements and build an OpenEnv environment around it.

Example of what a problem statement looks like

"Build a mini-game RL environment with clearly defined tasks, automated graders, and reward logic using the OpenEnv framework."

→ Create a mini-game an AI agent can play

→ Define tasks with increasing difficulty

→ Write graders that verify task completion

→ Define reward logic for scoring

→ Package using OpenEnv for automated evaluation

Evaluation Criteria

Runtime correctness

Runs without errors

Interface compliance

Follows OpenEnv standard

Task design

 Clear, realistic, testable

Grading logic

 Reward system makes sense