Instructions to use Louisdlms/a2c-PandaReachJointsDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Louisdlms/a2c-PandaReachJointsDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Louisdlms/a2c-PandaReachJointsDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
A2C PandaReachJointsDense-v3
This repository contains a Stable Baselines3 implementation of the Advantage Actor-Critic (A2C) algorithm trained on the PandaReachJointsDense-v3 environment from panda-gym. The model was trained for 500,000 timesteps to learn how to reach points in 3D space by controlling the robot's articulations.
Video Preview
Direct link: https://huggingface.co/Louisdlms/a2c-PandaReachJointsDense-v3/resolve/main/videos/panda_reach_result.mp4
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