Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use saisowrya/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use saisowrya/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="saisowrya/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.
Evaluation Results
- Mean Reward: -0.24 +/- 0.14
- Score (mean - std): -0.38 (Required: >= -3.5)
Usage
import gymnasium as gym
import panda_gym
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub
repo_id = "saisowrya/a2c-PandaReachDense-v3"
filename = "a2c-PandaReachDense-v3.zip"
model = load_from_hub(repo_id=repo_id, filename=filename)
- Downloads last month
- 8
Evaluation results
- mean_reward on PandaReachDense-v3self-reported-0.24 +/- 0.14