Reinforcement Learning
stable-baselines3
InvertedPendulum-v5
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use farama-minari/InvertedPendulum-v5-SAC-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use farama-minari/InvertedPendulum-v5-SAC-medium with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="farama-minari/InvertedPendulum-v5-SAC-medium", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
metadata
library_name: stable-baselines3
tags:
- InvertedPendulum-v5
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: InvertedPendulum-v5
type: InvertedPendulum-v5
metrics:
- type: mean_reward
value: 131.00 +/- 18.26
name: mean_reward
verified: false
SAC Agent playing InvertedPendulum-v5
This is a trained model of a SAC agent playing InvertedPendulum-v5 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...