Reinforcement Learning
ml-agents
TensorBoard
ONNX
ML-Agents-Pyramids
deep-reinforcement-learning
unity-ml-agents
Eval Results (legacy)
Instructions to use MUTTERAHUL/MLAgents-Pyramids with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use MUTTERAHUL/MLAgents-Pyramids with ml-agents:
mlagents-load-from-hf --repo-id="MUTTERAHUL/MLAgents-Pyramids" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
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Download README.md from MUTTERAHUL/MLAgents-Pyramids: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/MUTTERAHUL/MLAgents-Pyramids/resolve/main/README.md
- Command line
-
hf download hf://MUTTERAHUL/MLAgents-Pyramids/README.md
-
curl -L -o README.md https://huggingface.co/MUTTERAHUL/MLAgents-Pyramids/resolve/main/README.md
1.31 kB
metadata
tags:
- ml-agents
- ML-Agents-Pyramids
- reinforcement-learning
- deep-reinforcement-learning
- unity-ml-agents
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ML-Agents-Pyramids
type: ML-Agents-Pyramids
metrics:
- type: mean_reward
value: 15.00 +/- 2.00
name: mean_reward
verified: false
ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids 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-Pyramids
- Step 1: Write your model_id: MUTTERAHUL/MLAgents-Pyramids
- Step 2: Select your .nn /.onnx file
- Click on Watch the agent play 👀