Reinforcement Learning
ml-agents
TensorBoard
ONNX
ML-Agents-Pyramids
deep-reinforcement-learning
unity-ml-agents
Eval Results (legacy)
Instructions to use RBhumi/ppo-Pyramids with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use RBhumi/ppo-Pyramids with ml-agents:
mlagents-load-from-hf --repo-id="RBhumi/ppo-Pyramids" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
Download configuration.yaml from RBhumi/ppo-Pyramids: direct link, hf CLI and curl.
- Browser
- Download file 598 Bytes
-
https://huggingface.co/RBhumi/ppo-Pyramids/resolve/main/configuration.yaml
- Command line
-
hf download hf://RBhumi/ppo-Pyramids/configuration.yaml
-
curl -L -o configuration.yaml https://huggingface.co/RBhumi/ppo-Pyramids/resolve/main/configuration.yaml
598 Bytes
| behaviors: | |
| SnowballTarget: | |
| trainer_type: ppo | |
| summary_freq: 10000 | |
| keep_checkpoints: 10 | |
| checkpoint_interval: 50000 | |
| max_steps: 200000 | |
| time_horizon: 64 | |
| threaded: true | |
| 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 | |