Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use roshana1s/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use roshana1s/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="roshana1s/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
A2C Agent playing PandaReachDense-v3
This is a trained A2C agent playing PandaReachDense-v3 using the Stable-Baselines3 library.
Model
- Algorithm: A2C
- Environment: PandaReachDense-v3
- Library: Stable-Baselines3
- Task: Robotic arm reaching a target position
Usage with Stable-Baselines3
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub
checkpoint = load_from_hub(
repo_id="roshana1s/a2c-PandaReachDense-v3",
filename="a2c-PandaReachDense-v3.zip"
)
model = A2C.load(checkpoint)
The trained agent learns to control the Panda robotic arm and move its end-effector toward the target position while maximizing the environment reward.
Repository
Model available on Hugging Face:
roshana1s/a2c-PandaReachDense-v3
- Downloads last month
- 27
Evaluation results
- mean_reward on PandaReachDense-v3self-reported-0.23 +/- 0.13