README.md

---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---

  # **ppo** Agent playing **SnowballTarget**
  This is a trained model of a **ppo** agent playing **SnowballTarget**
  using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).

  ## Results
  -[INFO] SnowballTarget. 
  -Step: 400000. 
  -Time Elapsed: 903.639 s. 
  -Mean Reward: 25.591. 
  -Std of Reward: 1.992.

  ## Hyperparameters
  %%file /content/ml-agents/config/ppo/SnowballTarget.yaml
```yaml
behaviors:
  SnowballTarget:
    trainer_type: ppo
    summary_freq: 10000
    keep_checkpoints: 10
    checkpoint_interval: 50000
    max_steps: 400000
    time_horizon: 32
    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: 3
      vis_encode_type: nature_cnn
    reward_signals:
      extrinsic:
        gamma: 0.9
        strength: 1.0
```

  ### Resume the training
  ```bash
  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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
  2. Step 1: Find your model_id: enrique2701/ppo-SnowballTarget
  3. Step 2: Select your *.nn /*.onnx file
  4. Click on Watch the agent play 👀