
# **TQC** Agent playing **parking-v0**
This is a trained model of a **TQC** agent playing **parking-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.

## Usage (with SB3 RL Zoo)

RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```

```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo tqc --env parking-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env parking-v0  -f logs/
```

If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo tqc --env parking-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env parking-v0  -f logs/
```

## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo tqc --env parking-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env parking-v0 -f logs/ -orga qgallouedec
```

## Hyperparameters
```python
OrderedDict([('batch_size', 512),
             ('buffer_size', 1000000),
             ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
             ('gamma', 0.98),
             ('learning_rate', 0.0015),
             ('n_timesteps', 50000.0),
             ('policy', 'MultiInputPolicy'),
             ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
             ('replay_buffer_class', 'HerReplayBuffer'),
             ('replay_buffer_kwargs',
              "dict( online_sampling=False, goal_selection_strategy='episode', "
              'n_sampled_goal=4, max_episode_length=100 )'),
             ('tau', 0.005),
             ('normalize', False)])
```

{"library_name": "stable-baselines3", "tags": ["parking-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TQC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "parking-v0", "type": "parking-v0"}, "metrics": [{"type": "mean_reward", "value": "-8.40 +/- 4.19", "name": "mean_reward", "verified": false}]}]}]}
stable-baselines3 parking-v0 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #parking-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# TQC Agent playing parking-v0
This is a trained model of a TQC agent playing parking-v0
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.

## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL

Install the RL Zoo (with SB3 and SB3-Contrib):




If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:


## Training (with the RL Zoo)


## Hyperparameters


# TQC Agent playing parking-v0
This is a trained model of a TQC agent playing parking-v0
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL

Install the RL Zoo (with SB3 and SB3-Contrib):




If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters
TAGS
#stable-baselines3 #parking-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # TQC Agent playing parking-v0
This is a trained model of a TQC agent playing parking-v0
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL

Install the RL Zoo (with SB3 and SB3-Contrib):




If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters
TAGS
#stable-baselines3 #parking-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# TQC Agent playing parking-v0
This is a trained model of a TQC agent playing parking-v0
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL

Install the RL Zoo (with SB3 and SB3-Contrib):




If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters