lambdavi/ppo-SnowballTarget

ppo Agent playing SnowballTarget

This is a trained model of a ppo agent playing SnowballTarget 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. 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