Reinforcement Learning
Keras
TensorBoard
stable-baselines3
power-grid
ppo
lstm
electricity
forecasting
tensorflow
gym
Instructions to use jacksonferrigno/Grid_AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use jacksonferrigno/Grid_AI with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://jacksonferrigno/Grid_AI") - stable-baselines3
How to use jacksonferrigno/Grid_AI with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="jacksonferrigno/Grid_AI", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "stable-baselines3", | |
| "library": "stable-baselines3", | |
| "algorithm": "PPO", | |
| "env": "PowerGridEnv-v0", | |
| "vectorized": true, | |
| "vecnormalize": true, | |
| "observation_space": "Box(-1.0, 1.0, (40,), float32)", | |
| "action_space": "Box(-1.0, 1.0, (40,), float32)", | |
| "total_timesteps": 400000, | |
| "eval_episodes": 5, | |
| "eval_frequency": 2048, | |
| "best_model_saved": true, | |
| "reward_normalized": true, | |
| "clip_reward": 10.0, | |
| "trained_on": "Lauderdale County grid (TVA, Jan 2024)" | |
| } | |