Reinforcement Learning
stable-baselines3
deep-reinforcement-learning
agricultural-ai
weather-modelling
curriculum-learning
edge-ai
Instructions to use DHDRL/monsoon-rl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use DHDRL/monsoon-rl with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="DHDRL/monsoon-rl", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Update test_helio_obs.py
Browse files- test_helio_obs.py +1 -6
test_helio_obs.py
CHANGED
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@@ -4,11 +4,6 @@ test_helio_obs.py
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=================
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Diagnostic: prove SWPC helio reaches the RL observation vector, and that
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quiet vs storm BasinContext produces a measurable basin_context delta.
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Does NOT claim deterministic crop_risk_scorer benefit (scorer has no helio
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term by design until a validated tropical crop mechanism is specified).
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Run on Kaggle (ROOT on PYTHONPATH) or offline with synthetic BasinContext.
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"""
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from __future__ import annotations
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@@ -169,4 +164,4 @@ def main() -> int:
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if __name__ == "__main__":
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raise SystemExit(main())
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=================
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Diagnostic: prove SWPC helio reaches the RL observation vector, and that
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quiet vs storm BasinContext produces a measurable basin_context delta.
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"""
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from __future__ import annotations
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if __name__ == "__main__":
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raise SystemExit(main())
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