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---
tags:
- ALE/Pitfall2-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: ALE/Pitfall2-v5
      type: ALE/Pitfall2-v5
    metrics:
    - type: mean_reward
      value: 0.00 +/- 0.00
      name: mean_reward
      verified: false
---

# (CleanRL) **DQN** Agent Playing **ALE/Pitfall2-v5**

This is a trained model of a DQN agent playing ALE/Pitfall2-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/PitFall.py).

## Get Started

To use this model, please install the `cleanrl` package with the following command:

```

pip install "cleanrl[PitFall]"

python -m cleanrl_utils.enjoy --exp-name PitFall --env-id ALE/Pitfall2-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/cotran2/PitFall/raw/main/dqn_atari.py

curl -OL https://huggingface.co/cotran2/PitFall/raw/main/pyproject.toml

curl -OL https://huggingface.co/cotran2/PitFall/raw/main/poetry.lock

poetry install --all-extras

python dqn_atari.py --exp-name PitFall --track --wandb-project-name PitFall --capture-video --env-id ALE/Pitfall2-v5 --total-timesteps 1000000 --buffer-size 400000 --save-model True --upload-model True --hf-entity cotran2

```

# Hyperparameters
```python

{'batch_size': 32,

 'buffer_size': 400000,

 'capture_video': True,

 'cuda': True,

 'end_e': 0.01,

 'env_id': 'ALE/Pitfall2-v5',

 'exp_name': 'PitFall',

 'exploration_fraction': 0.1,

 'gamma': 0.99,

 'hf_entity': 'cotran2',

 'learning_rate': 0.0001,

 'learning_starts': 80000,

 'num_envs': 1,

 'save_model': True,

 'seed': 1,

 'start_e': 1,

 'target_network_frequency': 1000,

 'tau': 1.0,

 'torch_deterministic': True,

 'total_timesteps': 1000000,

 'track': True,

 'train_frequency': 4,

 'upload_model': True,

 'wandb_entity': None,

 'wandb_project_name': 'PitFall'}

```