Reinforcement Learning
ml-agents
TensorBoard
ONNX
SnowballTarget
deep-reinforcement-learning
ML-Agents-SnowballTarget
Instructions to use lambdavi/ppo-SnowballTarget with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use lambdavi/ppo-SnowballTarget with ml-agents:
mlagents-load-from-hf --repo-id="lambdavi/ppo-SnowballTarget" --local-dir="./download: string[]s"
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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2. Step 1: Find your model_id: lambdavi/ppo-SnowballTarget
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3. Step 2: Select your *.nn /*.onnx file
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4. Click on Watch the agent play 👀
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2. Step 1: Find your model_id: lambdavi/ppo-SnowballTarget
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3. Step 2: Select your *.nn /*.onnx file
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4. Click on Watch the agent play 👀
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### Hyperparams used:
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SnowballTarget:
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trainer_type: ppo
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hyperparameters:
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batch_size: 128
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buffer_size: 2048
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learning_rate: 0.005
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beta: 0.005
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epsilon: 0.2
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lambd: 0.95
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num_epoch: 5
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shared_critic: False
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learning_rate_schedule: linear
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beta_schedule: linear
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epsilon_schedule: linear
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checkpoint_interval: 50000
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network_settings:
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normalize: False
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hidden_units: 256
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num_layers: 2
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vis_encode_type: simple
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memory: None
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goal_conditioning_type: hyper
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deterministic: False
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reward_signals:
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extrinsic:
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gamma: 0.99
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strength: 1.0
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network_settings:
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normalize: False
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hidden_units: 128
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num_layers: 2
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vis_encode_type: simple
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memory: None
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goal_conditioning_type: hyper
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deterministic: False
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init_path: None
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keep_checkpoints: 10
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even_checkpoints: False
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max_steps: 500000
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time_horizon: 64
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summary_freq: 10000
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threaded: True
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self_play: None
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behavioral_cloning: None
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