
# **TQC** Agent playing **PandaPush-v1**
This is a trained model of a **TQC** agent playing **PandaPush-v1**
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

```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo tqc --env PandaPush-v1 -orga sb3 -f logs/
python enjoy.py --algo tqc --env PandaPush-v1  -f logs/
```

## Training (with the RL Zoo)
```
python train.py --algo tqc --env PandaPush-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env PandaPush-v1 -f logs/ -orga sb3
```

## Hyperparameters
```python
OrderedDict([('batch_size', 2048),
             ('buffer_size', 1000000),
             ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
             ('gamma', 0.95),
             ('learning_rate', 0.001),
             ('n_timesteps', 1000000.0),
             ('policy', 'MultiInputPolicy'),
             ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
             ('replay_buffer_class', 'HerReplayBuffer'),
             ('replay_buffer_kwargs',
              "dict( online_sampling=True, goal_selection_strategy='future', "
              'n_sampled_goal=4, )'),
             ('tau', 0.05),
             ('normalize', False)])
```

Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
{"library_name": "stable-baselines3", "tags": ["PandaPush-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TQC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaPush-v1", "type": "PandaPush-v1"}, "metrics": [{"type": "mean_reward", "value": "-7.00 +/- 1.79", "name": "mean_reward"}]}]}]}
stable-baselines3 PandaPush-v1 deep-reinforcement-learning reinforcement-learning arxiv:2106.13687 model-index region:us
TAGS
#stable-baselines3 #PandaPush-v1 #deep-reinforcement-learning #reinforcement-learning #arxiv-2106.13687 #model-index #region-us 


# TQC Agent playing PandaPush-v1
This is a trained model of a TQC agent playing PandaPush-v1
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



## Training (with the RL Zoo)


## Hyperparameters


Panda Gym environments: URL
# TQC Agent playing PandaPush-v1
This is a trained model of a TQC agent playing PandaPush-v1
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 ## Training (with the RL Zoo) ## Hyperparameters


Panda Gym environments: URL
TAGS
#stable-baselines3 #PandaPush-v1 #deep-reinforcement-learning #reinforcement-learning #arxiv-2106.13687 #model-index #region-us 
 # TQC Agent playing PandaPush-v1
This is a trained model of a TQC agent playing PandaPush-v1
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 ## Training (with the RL Zoo) ## Hyperparameters


Panda Gym environments: URL
TAGS
#stable-baselines3 #PandaPush-v1 #deep-reinforcement-learning #reinforcement-learning #arxiv-2106.13687 #model-index #region-us 
# TQC Agent playing PandaPush-v1
This is a trained model of a TQC agent playing PandaPush-v1
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## Training (with the RL Zoo)## Hyperparameters


Panda Gym environments: URL