Reinforcement Learning
stable-baselines3
PandaReachDense
PandaReachDense-v3
panda-gym
deep-rl-course
a2c
Eval Results (legacy)
Instructions to use Learnix-AI-Lab/a2c-PandaReachDense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Learnix-AI-Lab/a2c-PandaReachDense with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Learnix-AI-Lab/a2c-PandaReachDense", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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Download README.md from Learnix-AI-Lab/a2c-PandaReachDense: direct link, hf CLI and curl.
- Browser
- Download file 700 Bytes
-
https://huggingface.co/Learnix-AI-Lab/a2c-PandaReachDense/resolve/main/README.md
- Command line
-
hf download hf://Learnix-AI-Lab/a2c-PandaReachDense/README.md
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curl -L -o README.md https://huggingface.co/Learnix-AI-Lab/a2c-PandaReachDense/resolve/main/README.md
700 Bytes
metadata
tags:
- PandaReachDense
- PandaReachDense-v3
- panda-gym
- deep-rl-course
- a2c
- reinforcement-learning
- stable-baselines3
library_name: stable-baselines3
pipeline_tag: reinforcement-learning
model-index:
- name: a2c-PandaReachDense
results:
- task:
name: reinforcement-learning
type: reinforcement-learning
dataset:
name: PandaReachDense
type: PandaReachDense
metrics:
- name: mean_reward
type: mean_reward
value: '-0.45 +/- 0.12'
A2C Agent playing PandaReachDense
This is a trained model of an A2C agent playing PandaReachDense using stable-baselines3 and panda-gym.
- Mean Reward: -0.45 +/- 0.12
- Result (mean - std): -0.57