---
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).

  ## 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
  ```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: lambdavi/ppo-SnowballTarget
  3. Step 2: Select your *.nn /*.onnx file
  4. Click on Watch the agent play 👀

  ### Hyperparams used:
  ```
  SnowballTarget: 
  	trainer_type:	ppo
  	hyperparameters:	
  	  batch_size:	128
  	  buffer_size:	2048
  	  learning_rate:	0.005
  	  beta:	0.005
  	  epsilon:	0.2
  	  lambd:	0.95
  	  num_epoch:	5
  	  shared_critic:	False
  	  learning_rate_schedule:	linear
  	  beta_schedule:	linear
  	  epsilon_schedule:	linear
  	checkpoint_interval:	50000
  	network_settings:	
  	  normalize:	False
  	  hidden_units:	256
  	  num_layers:	2
  	  vis_encode_type:	simple
  	  memory:	None
  	  goal_conditioning_type:	hyper
  	  deterministic:	False
  	reward_signals:	
  	  extrinsic:	
  	    gamma:	0.99
  	    strength:	1.0
  	    network_settings:	
  	      normalize:	False
  	      hidden_units:	128
  	      num_layers:	2
  	      vis_encode_type:	simple
  	      memory:	None
  	      goal_conditioning_type:	hyper
  	      deterministic:	False
  	init_path:	None
  	keep_checkpoints:	10
  	even_checkpoints:	False
  	max_steps:	500000
  	time_horizon:	64
  	summary_freq:	10000
  	threaded:	True
  	self_play:	None
  	behavioral_cloning:	None  
 ```