Reinforcement Learning
stable-baselines3
PandaReachDense-v3
PandaReachDense
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use SimhaSimha/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use SimhaSimha/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="SimhaSimha/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download evaluation.json from SimhaSimha/a2c-PandaReachDense-v3: direct link, hf CLI and curl.
- Browser
- Download file 286 Bytes
-
https://huggingface.co/SimhaSimha/a2c-PandaReachDense-v3/resolve/main/evaluation.json
- Command line
-
hf download hf://SimhaSimha/a2c-PandaReachDense-v3/evaluation.json
-
curl -L -o evaluation.json https://huggingface.co/SimhaSimha/a2c-PandaReachDense-v3/resolve/main/evaluation.json
286 Bytes
| { | |
| "env_id": "PandaReachDense-v3", | |
| "episodes": 10, | |
| "seed": 42, | |
| "mean_reward": -10.995390875078737, | |
| "std_reward": 14.42657599343377, | |
| "score_mean_minus_std": -25.421966868512506, | |
| "success_rate": 0.5, | |
| "min_reward": -42.178568080067635, | |
| "max_reward": -0.04786994680762291 | |
| } |