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 zone_observation.py
Browse files- zone_observation.py +6 -0
zone_observation.py
CHANGED
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@@ -167,6 +167,7 @@ class ForecastConfig:
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clean_episode_ratio: float = 0.7
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event_spatial_correlation: float = 0.85
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shuffle_zone_order: bool = True
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alert_value_range: Tuple[float, float] = (0.8, 1.2)
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miss_penalty_range: Tuple[float, float] = (0.9, 1.1)
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@@ -243,6 +244,10 @@ class ForecastConfig:
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raise ValueError(
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f"ForecastConfig: real_data_ratio={self.real_data_ratio} must be in [0, 1]"
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)
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if not (0.0 <= self.era5_ratio <= 1.0):
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raise ValueError(
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f"ForecastConfig: era5_ratio={self.era5_ratio} must be in [0, 1]"
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@@ -320,6 +325,7 @@ class ForecastConfig:
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"clean_episode_ratio": self.clean_episode_ratio,
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"event_spatial_correlation": self.event_spatial_correlation,
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"shuffle_zone_order": self.shuffle_zone_order,
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"alert_value_range": list(self.alert_value_range),
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"miss_penalty_range": list(self.miss_penalty_range),
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"soft_reset": self.soft_reset,
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clean_episode_ratio: float = 0.7
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event_spatial_correlation: float = 0.85
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shuffle_zone_order: bool = True
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+
forecast_flag_error_rate: float = 0.0
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alert_value_range: Tuple[float, float] = (0.8, 1.2)
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miss_penalty_range: Tuple[float, float] = (0.9, 1.1)
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raise ValueError(
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f"ForecastConfig: real_data_ratio={self.real_data_ratio} must be in [0, 1]"
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)
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if not (0.0 <= self.forecast_flag_error_rate <= 1.0):
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raise ValueError(
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f'ForecastConfig: forecast_flag_error_rate={self.forecast_flag_error_rate} must be in [0, 1]'
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)
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if not (0.0 <= self.era5_ratio <= 1.0):
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raise ValueError(
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f"ForecastConfig: era5_ratio={self.era5_ratio} must be in [0, 1]"
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"clean_episode_ratio": self.clean_episode_ratio,
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"event_spatial_correlation": self.event_spatial_correlation,
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"shuffle_zone_order": self.shuffle_zone_order,
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+
"forecast_flag_error_rate": self.forecast_flag_error_rate,
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"alert_value_range": list(self.alert_value_range),
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"miss_penalty_range": list(self.miss_penalty_range),
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"soft_reset": self.soft_reset,
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