Reinforcement Learning
ml-agents
TensorBoard
ONNX
SnowballTarget
deep-reinforcement-learning
ML-Agents-SnowballTarget
Instructions to use shash0609/ppo-SnowballTarget-programmatic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use shash0609/ppo-SnowballTarget-programmatic with ml-agents:
mlagents-load-from-hf --repo-id="shash0609/ppo-SnowballTarget-programmatic" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
Download config.json from shash0609/ppo-SnowballTarget-programmatic: direct link, hf CLI and curl.
- Browser
- Download file 555 Bytes
-
https://huggingface.co/shash0609/ppo-SnowballTarget-programmatic/resolve/main/config.json
- Command line
-
hf download hf://shash0609/ppo-SnowballTarget-programmatic/config.json
-
curl -L -o config.json https://huggingface.co/shash0609/ppo-SnowballTarget-programmatic/resolve/main/config.json
555 Bytes
| {"behaviors": {"SnowballTarget": {"trainer_type": "ppo", "summary_freq": 10000, "keep_checkpoints": 10, "checkpoint_interval": 50000, "max_steps": 200000, "time_horizon": 64, "threaded": false, "hyperparameters": {"learning_rate": 0.0003, "learning_rate_schedule": "linear", "batch_size": 128, "buffer_size": 2048, "beta": 0.005, "epsilon": 0.2, "lambd": 0.95, "num_epoch": 3}, "network_settings": {"normalize": false, "hidden_units": 256, "num_layers": 2, "vis_encode_type": "simple"}, "reward_signals": {"extrinsic": {"gamma": 0.99, "strength": 1.0}}}}} |