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---
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](https://github.com/Unity-Technologies/ml-agents).
## 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
```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
```bash
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: enrique2701/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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