Reinforcement Learning
stable-baselines3
CartPole-v1
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use thomasbar/a2c_sb3_cartpole with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use thomasbar/a2c_sb3_cartpole with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="thomasbar/a2c_sb3_cartpole", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
MlpPolicy Agent playing CartPole-v1
This is a trained model of a MlpPolicy agent playing CartPole-v1 using the stable-baselines3 library.
Usage (with Stable-baselines3)
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub
checkpoint = load_from_hub(
repo_id="thomasbar/a2c_sb3_cartpole",
filename="a2c_sb3_cartpole.zip",
)
model = A2C.load(checkpoint)
- Downloads last month
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Evaluation results
- mean_reward on CartPole-v1self-reported500.00 +/- 0.00