Reinforcement Learning
ml-agents
TensorBoard
ONNX
ML-Agents-Pyramids
deep-rl-class
Eval Results (legacy)
Instructions to use Vladprime/ppo-PyramidsRND with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use Vladprime/ppo-PyramidsRND with ml-agents:
mlagents-load-from-hf --repo-id="Vladprime/ppo-PyramidsRND" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
Download config.yaml from Vladprime/ppo-PyramidsRND: direct link, hf CLI and curl.
- Browser
- Download file 730 Bytes
-
https://huggingface.co/Vladprime/ppo-PyramidsRND/resolve/main/config.yaml
- Command line
-
hf download hf://Vladprime/ppo-PyramidsRND/config.yaml
-
curl -L -o config.yaml https://huggingface.co/Vladprime/ppo-PyramidsRND/resolve/main/config.yaml
730 Bytes
| behaviors: | |
| Pyramids: | |
| trainer_type: ppo | |
| hyperparameters: | |
| batch_size: 128 | |
| buffer_size: 2048 | |
| learning_rate: 0.0003 | |
| beta: 0.01 | |
| epsilon: 0.2 | |
| lambd: 0.95 | |
| num_epoch: 3 | |
| learning_rate_schedule: linear | |
| network_settings: | |
| normalize: false | |
| hidden_units: 128 | |
| num_layers: 2 | |
| vis_encode_type: simple | |
| reward_signals: | |
| extrinsic: | |
| gamma: 0.99 | |
| strength: 1.0 | |
| rnd: | |
| gamma: 0.99 | |
| strength: 0.01 | |
| network_settings: | |
| hidden_units: 64 | |
| num_layers: 3 | |
| learning_rate: 0.0001 | |
| keep_checkpoints: 2 | |
| max_steps: 30000 | |
| time_horizon: 128 | |
| summary_freq: 2000 | |
| checkpoint_interval: 10000 | |