Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Yujana/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Yujana/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Yujana/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
A2C Agent playing PandaReachDense-v3
This is a trained model of an A2C agent playing PandaReachDense-v3 using the stable-baselines3 library and panda-gym.
Evaluation Results
- Mean Reward: -0.20 +/- 0.09
- Score (Mean - Std): -0.29 (Requirement: >= -3.5)
Usage (with Stable-baselines3)
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub
checkpoint = load_from_hub(
repo_id="Yujana/a2c-PandaReachDense-v3",
filename="a2c-PandaReachDense-v3.zip",
)
model = A2C.load(checkpoint)
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Evaluation results
- mean_reward on PandaReachDense-v3self-reported-0.20 +/- 0.09