Reinforcement Learning
stable-baselines3
MountainCarContinuous-v0
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use danieladejumo/ppo-mountan_car_continuous with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use danieladejumo/ppo-mountan_car_continuous with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="danieladejumo/ppo-mountan_car_continuous", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent playing MountainCarContinuous-v0
This is a trained model of a PPO agent playing MountainCarContinuous-v0 using the stable-baselines3 library.
Usage (with Stable-baselines3)
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
# load and create the model
model_path = load_from_hub("danieladejumo/ppo-mountan_car_continuous",
"ppo-mountan_car_continuous.zip")
model = PPO.load(model_path)
# create Mountain Car Continuous environment and evaluate the trained agent
env = gym.make("MountainCarContinuous-v0")
mean_return, std_return = evaluate_policy(model, env, n_eval_episodes=50, deterministic=True)
- Downloads last month
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Evaluation results
- mean_reward on MountainCarContinuous-v0self-reported-0.00 +/- 0.00
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="danieladejumo/ppo-mountan_car_continuous", filename="{MODEL FILENAME}.zip", )