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
File size: 804 Bytes
9195c78 b604b08 9195c78 b604b08 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | # Core (needed for the data/scoring/Indonesia stack -- climatology.py, # indonesia_zones.py, backtest_indonesia.py, era5_data_pipeline.py, # zone_observation.py, crop_risk_scorer.py, hierarchical_search.py) numpy>=1.24 # RL training stack (train_curriculum.py, train_kaggle.py, # weather_forecast_env.py, gru_weather_policy.py) torch>=2.0 gymnasium>=0.29 stable-baselines3>=2.0 sb3-contrib>=2.0 # Hyperparameter sweeps (sweep_reward_shaping.py) optuna>=3.5 # Edge export (mnn_export.py) -- MNN itself has no pip package; build/install # per https://github.com/alibaba/MNN, this only covers the ONNX/export side. onnx>=1.15 # Optional -- only needed if you actually connect to a broker # (node_transport.py's MQTTTransport falls back to LocalTransport without it) paho-mqtt>=1.6 # Testing pytest>=7.0 |