Reinforcement Learning
stable-baselines3
PandaReachDense-v3
a2c
panda-gym
deep-rl-class
Eval Results (legacy)
Instructions to use jnforja/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use jnforja/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="jnforja/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
| { | |
| "env_id": "PandaReachDense-v3", | |
| "repo_id": "jnforja/a2c-PandaReachDense-v3", | |
| "model_name": "a2c-PandaReachDense-v3", | |
| "seed": 42, | |
| "n_envs": 4, | |
| "total_timesteps": 1000000, | |
| "policy": "MultiInputPolicy", | |
| "model_architecture": "A2C", | |
| "norm_obs": true, | |
| "norm_reward": true, | |
| "clip_obs": 10.0, | |
| "eval_episodes": 10, | |
| "video_episodes": 1, | |
| "min_video_seconds": 3 | |
| } |