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---
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 40.50 +/- 23.82
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4.
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/dqn30may_rnd.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[dqn30may_rnd]"
python -m cleanrl_utils.enjoy --exp-name dqn30may_rnd --env-id SpaceInvadersNoFrameskip-v4
```
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/FranEnguix/SpaceInvadersNoFrameskip-v4-dqn30may_rnd-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/FranEnguix/SpaceInvadersNoFrameskip-v4-dqn30may_rnd-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/FranEnguix/SpaceInvadersNoFrameskip-v4-dqn30may_rnd-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --env-id SpaceInvadersNoFrameskip-v4 --track --cuda --total-timesteps 0 --buffer-size 800000 --capture-video --upload-model --hf-entity FranEnguix --exp-name dqn30may_rnd --wandb-project-name arf_final_project --seed 1 --save-model
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 800000,
'capture_video': True,
'cuda': True,
'end_e': 0.01,
'env_id': 'SpaceInvadersNoFrameskip-v4',
'exp_name': 'dqn30may_rnd',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'FranEnguix',
'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': 0,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'arf_final_project'}
```
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