Reinforcement Learning
stable-baselines3
AntBulletEnv-v0
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Patil/a2c-AntBulletEnv-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Patil/a2c-AntBulletEnv-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Patil/a2c-AntBulletEnv-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Patil/a2c-AntBulletEnv-v0: direct link, hf CLI and curl.
- Browser
- Download file 791 Bytes
-
https://huggingface.co/Patil/a2c-AntBulletEnv-v0/resolve/main/README.md
- Command line
-
hf download hf://Patil/a2c-AntBulletEnv-v0/README.md
-
curl -L -o README.md https://huggingface.co/Patil/a2c-AntBulletEnv-v0/resolve/main/README.md
791 Bytes
metadata
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1887.24 +/- 117.10
name: mean_reward
verified: false
A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...