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.
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: lambdavi/ppo-SnowballTarget
- Step 2: Select your .nn /.onnx file
- 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