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
File size: 598 Bytes
b710b82 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 |
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
|