Instructions to use Ravikanth8788/ppo-Pyramids with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use Ravikanth8788/ppo-Pyramids with ml-agents:
mlagents-load-from-hf --repo-id="Ravikanth8788/ppo-Pyramids" --local-dir="./download: string[]s"
- Notebooks
- Google Colab
- Kaggle
Download config.json from Ravikanth8788/ppo-Pyramids: direct link, hf CLI and curl.
- Browser
- Download file 617 Bytes
-
https://huggingface.co/Ravikanth8788/ppo-Pyramids/resolve/main/config.json
- Command line
-
hf download hf://Ravikanth8788/ppo-Pyramids/config.json
-
curl -L -o config.json https://huggingface.co/Ravikanth8788/ppo-Pyramids/resolve/main/config.json
617 Bytes
| {"behaviors": {"Pyramids": {"trainer_type": "ppo", "hyperparameters": {"batch_size": 128, "buffer_size": 2048, "learning_rate": 0.0003, "beta": 0.005, "epsilon": 0.2, "lambd": 0.95, "num_epoch": 3, "learning_rate_schedule": "linear"}, "network_settings": {"normalize": false, "hidden_units": 512, "num_layers": 2, "vis_encode_type": "simple"}, "reward_signals": {"extrinsic": {"gamma": 0.99, "strength": 1.0}, "curiosity": {"gamma": 0.99, "strength": 0.02, "network_settings": {"hidden_units": 256}, "learning_rate": 0.0003}}, "keep_checkpoints": 5, "max_steps": 1000000, "time_horizon": 128, "summary_freq": 10000}}} |