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---
tags:
- Pong-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v5
type: Pong-v5
metrics:
- type: mean_reward
value: -20.30 +/- 0.78
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Pong-v5**
This is a trained model of a PPO agent playing Pong-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_xla_jax_scan.py).
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/vwxyzjn/Pong-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/ppo_atari_envpool_xla_jax_scan.py
curl -OL https://huggingface.co/vwxyzjn/Pong-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/vwxyzjn/Pong-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_xla_jax_scan.py --save-model --total-timesteps 1025 --upload-model
```
# Hyperparameters
```python
{'anneal_lr': True,
'batch_size': 1024,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Pong-v5',
'exp_name': 'ppo_atari_envpool_xla_jax_scan',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': '',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 256,
'norm_adv': True,
'num_envs': 8,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 1,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 1025,
'track': False,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
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