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 train_curriculum.py
Browse files- train_curriculum.py +5 -0
train_curriculum.py
CHANGED
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@@ -702,6 +702,11 @@ def train_phase(
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if real_data_noise_scale is not None:
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config_kwargs["noise_scale"] = real_data_noise_scale
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config = ForecastConfig(**config_kwargs)
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phase.risk_weights.attach_to_config(config)
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if config.real_data_pkl_path:
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if real_data_noise_scale is not None:
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config_kwargs["noise_scale"] = real_data_noise_scale
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config = ForecastConfig(**config_kwargs)
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# Train cube v4 TIGGE control: precip_mm length 15.
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# Holdout persistence rows are still horizon 30. Do NOT copy this
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# into evaluate_checkpoint_real / backtest_indonesia.
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if config.real_data_pkl_path:
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config.horizon_days = 15
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phase.risk_weights.attach_to_config(config)
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if config.real_data_pkl_path:
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