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Hopper-v4-sac_continuous_action-seed4

Reinforcement Learning
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cleanrl
Hopper-v4
deep-reinforcement-learning
custom-implementation
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Hopper-v4-sac_continuous_action-seed4
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---
tags:
- Hopper-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: SAC
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: Hopper-v4
      type: Hopper-v4
    metrics:
    - type: mean_reward
      value: 2819.87 +/- 1081.94
      name: mean_reward
      verified: false
---

# (CleanRL) **SAC** Agent Playing **Hopper-v4**

This is a trained model of a SAC agent playing Hopper-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/sac_continuous_action.py).

## Get Started

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

```
pip install "cleanrl[sac_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name sac_continuous_action --env-id Hopper-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/sdpkjc/Hopper-v4-sac_continuous_action-seed4/raw/main/sac_continuous_action.py
curl -OL https://huggingface.co/sdpkjc/Hopper-v4-sac_continuous_action-seed4/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Hopper-v4-sac_continuous_action-seed4/raw/main/poetry.lock
poetry install --all-extras
python sac_continuous_action.py --save-model --upload-model --hf-entity sdpkjc --env-id Hopper-v4 --seed 4 --track
```

# Hyperparameters
```python
{'alpha': 0.2,
 'autotune': True,
 'batch_size': 256,
 'buffer_size': 1000000,
 'capture_video': False,
 'cuda': True,
 'env_id': 'Hopper-v4',
 'exp_name': 'sac_continuous_action',
 'gamma': 0.99,
 'hf_entity': 'sdpkjc',
 'learning_starts': 5000.0,
 'noise_clip': 0.5,
 'policy_frequency': 2,
 'policy_lr': 0.0003,
 'q_lr': 0.001,
 'save_model': True,
 'seed': 4,
 'target_network_frequency': 1,
 'tau': 0.005,
 'torch_deterministic': True,
 'total_timesteps': 1000000,
 'track': True,
 'upload_model': True,
 'wandb_entity': None,
 'wandb_project_name': 'cleanRL'}
```