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---
tags:
- Amidar-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: Amidar-v5
type: Amidar-v5
metrics:
- type: mean_reward
value: 422.80 +/- 57.35
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Amidar-v5**
This is a trained model of a PPO agent playing Amidar-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/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Amidar-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Amidar-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Amidar-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Amidar-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Amidar-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Amidar-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
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