watercolour-env / __init__.py
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Canonical env: reward weights from the environment, pool from the pinned dataset
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# SPDX-License-Identifier: BSD-3-Clause
"""Watercolour environment for OpenEnv.
The model writes a p5.brush sketch, the environment paints it in a headless
browser and compares the result against a pool of reference paintings. The
reward is comparative rather than absolute: a vision judge is asked which of
two paintings is the better watercolour, in both presentation orders, because
that is a question it answers stably and an absolute rating is not.
Examples:
```python
from envs.watercolour_env import WatercolourAction, WatercolourEnv
with WatercolourEnv(base_url="http://localhost:8000") as env:
observation = env.reset().observation
reply = my_model(observation.system_prompt, observation.prompt)
result = env.step(WatercolourAction(response=reply))
print(result.reward, result.observation.feedback)
```
"""
from .client import WatercolourEnv
from .models import WatercolourAction, WatercolourObservation, WatercolourState
__all__ = [
"WatercolourEnv",
"WatercolourAction",
"WatercolourObservation",
"WatercolourState",
]