Reinforcement Learning
stable-baselines3
Pendulum-v1
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use FelixMartins/ppo-Pendulum-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use FelixMartins/ppo-Pendulum-v1 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="FelixMartins/ppo-Pendulum-v1", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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Download README.md from FelixMartins/ppo-Pendulum-v1: direct link, hf CLI and curl.
- Browser
- Download file 771 Bytes
-
https://huggingface.co/FelixMartins/ppo-Pendulum-v1/resolve/main/README.md
- Command line
-
hf download hf://FelixMartins/ppo-Pendulum-v1/README.md
-
curl -L -o README.md https://huggingface.co/FelixMartins/ppo-Pendulum-v1/resolve/main/README.md
771 Bytes
metadata
library_name: stable-baselines3
tags:
- Pendulum-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pendulum-v1
type: Pendulum-v1
metrics:
- type: mean_reward
value: '-251.36 +/- 142.43'
name: mean_reward
verified: false
PPO Agent playing Pendulum-v1
This is a trained model of a PPO agent playing Pendulum-v1 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...