Reinforcement Learning
stable-baselines3
PandaReachDense-v3
A2C
panda-gym
deep-rl-course
Eval Results (legacy)
Instructions to use jos0054/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use jos0054/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="jos0054/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
A2C PandaReachDense-v3
Trained from scratch with four environments and observation/reward normalization. Independent 100-episode evaluation: -0.21 +/- 0.10; score -0.30.
Load the included vec_normalize.pkl with VecNormalize.load; set training=False and norm_reward=False for evaluation, then load a2c-PandaReachDense-v3.zip with A2C.load.
- Downloads last month
- 4
Evaluation results
- mean_reward on PandaReachDense-v3self-reported-0.21 +/- 0.10