chirbard/ppo-Worm

# ppo Agent playing Worm This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.

## 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

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

## Hyperparameters

  behaviors:
    Worm:
      trainer_type: ppo
      hyperparameters:
        batch_size: 2024
        buffer_size: 20240
        learning_rate: 0.0003
        beta: 0.005
        epsilon: 0.2
        lambd: 0.95
        num_epoch: 3
        learning_rate_schedule: linear
      network_settings:
        normalize: true
        hidden_units: 512
        num_layers: 3
        vis_encode_type: simple
      reward_signals:
        extrinsic:
          gamma: 0.9995
          strength: 1.0
      keep_checkpoints: 5
      max_steps: 5000000
      time_horizon: 1000
      summary_freq: 30000