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ppo-SnowballTarget
enrique2701/ppo-SnowballTarget
ppo-SnowballTarget is a reinforcement learning model from enrique2701. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for ml-agents.
This is a trained model of a ppo agent playing SnowballTarget using the Unity ML-Agents Library.
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At a glance
- Task
- Reinforcement Learning
- Library
- ml-agents
- Access
- Public
- Created
- Feb 25, 2024
- Updated
- Feb 25, 2024
- SHA
- cddf0719
Model card
From the Hugging Face model README
ppo Agent playing SnowballTarget
This is a trained model of a ppo agent playing SnowballTarget using the Unity ML-Agents Library.
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
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
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-SnowballTarget
- Step 2: Select your .nn /.onnx file
- Click on Watch the agent play 👀
- Task
- Reinforcement Learning
- Library
- ml-agents
- Created
- Feb 25, 2024
- Updated
- Feb 25, 2024