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---
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: C51
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
metrics:
- type: mean_reward
value: -200.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **C51** Agent Playing **MountainCar-v0**
This is a trained model of a C51 agent playing MountainCar-v0.
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/c51_jax.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[c51_jax]"
python -m cleanrl_utils.enjoy --exp-name c51_jax --env-id MountainCar-v0
```
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/MountainCar-v0-c51_jax-seed1/raw/main/c51_jax.py
curl -OL https://huggingface.co/cleanrl/MountainCar-v0-c51_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/MountainCar-v0-c51_jax-seed1/raw/main/poetry.lock
poetry install --all-extras
python c51_jax.py --save-model --upload-model --hf-entity cleanrl --env-id MountainCar-v0
```
# Hyperparameters
```python
{'batch_size': 128,
'buffer_size': 10000,
'capture_video': False,
'end_e': 0.05,
'env_id': 'MountainCar-v0',
'exp_name': 'c51_jax',
'exploration_fraction': 0.5,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'learning_starts': 10000,
'n_atoms': 101,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 500,
'total_timesteps': 500000,
'track': False,
'train_frequency': 10,
'upload_model': True,
'v_max': 100,
'v_min': -100,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
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