metadata
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
Results
[INFO] Pyramids. Step: 2320000. Time Elapsed: 4995.783 s. Mean Reward: 1.775. Std of Reward: 0.113.
Hyperparameters
%%file /content/ml-agents/config/ppo/PyramidsRND.yaml
behaviors:
Pyramids:
trainer_type: ppo
hyperparameters:
batch_size: 252
buffer_size: 4096
learning_rate: 0.0003
beta: 0.01
epsilon: 0.2
lambd: 0.95
num_epoch: 3
learning_rate_schedule: linear
network_settings:
normalize: false
hidden_units: 512
num_layers: 2
vis_encode_type: nature_cnn
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
rnd:
gamma: 0.99
strength: 0.01
network_settings:
hidden_units: 64
num_layers: 3
learning_rate: 0.0001
keep_checkpoints: 5
max_steps: 3000000
time_horizon: 512
summary_freq: 10000
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: enrique2701/ppo-Pyramids
- Step 2: Select your .nn /.onnx file
- Click on Watch the agent play 👀