%%file /content/ml-agents/config/ppo/PyramidsRND.yaml
behaviors:
Pyramids:
  trainer_type: ppo
  hyperparameters:
    batch_size: 252
    buffer_size: 4096
    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: 512
    num_layers: 2
    vis_encode_type: nature_cnn
  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: 5
  max_steps: 3000000
  time_horizon: 512
  summary_freq: 10000

Usage (with ML-Agents)

The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/

We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:

A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction

Resume the training

mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume

Watch your Agent play

You can watch your agent playing directly in your browser

If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
Step 1: Find your model_id: enrique2701/ppo-Pyramids
Step 2: Select your .nn /.onnx file
Click on Watch the agent play 👀

enrique2701/ppo-Pyramids

ppo Agent playing Pyramids

Results

Hyperparameters

Usage (with ML-Agents)

Resume the training

Watch your Agent play

enrique2701/
ppo-Pyramids