Reinforcement Learning
ml-agents
TensorBoard
ONNX
unity-ml-agents
deep-reinforcement-learning
ML-Agents-SnowballTarget
Instructions to use pyflynn/ppo-SnowballTarget with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use pyflynn/ppo-SnowballTarget with ml-agents:
mlagents-load-from-hf --repo-id="pyflynn/ppo-SnowballTarget" --local-dir="./download: string[]s"
- Notebooks
- Google Colab
- Kaggle
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Download README.md from pyflynn/ppo-SnowballTarget: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/pyflynn/ppo-SnowballTarget/resolve/main/README.md
- Command line
-
hf download hf://pyflynn/ppo-SnowballTarget/README.md
-
curl -L -o README.md https://huggingface.co/pyflynn/ppo-SnowballTarget/resolve/main/README.md
1.05 kB
metadata
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
ppo Agent playing SnowballTarget
This is a trained model of a ppo agent playing SnowballTarget using the Unity ML-Agents Library.
Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
Resume the training
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
Watch your Agent play
You can watch your agent playing directly in your browser:.
- Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
- Step 1: Write your model_id: pyflynn/ppo-SnowballTarget
- Step 2: Select your .nn /.onnx file
- Click on Watch the agent play 👀