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---
library_name: stable-baselines3
tags:
- PandaReach-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReach-v1
type: PandaReach-v1
metrics:
- type: mean_reward
value: -2.20 +/- 0.75
name: mean_reward
verified: false
---
# **TQC** Agent playing **PandaReach-v1**
This is a trained model of a **TQC** agent playing **PandaReach-v1** (arxiv.org/abs/2106.13687)
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 PandaReach-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env PandaReach-v1 -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 PandaReach-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env PandaReach-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo tqc --env PandaReach-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env PandaReach-v1 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('buffer_size', 1000000),
('ent_coef', 'auto'),
('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
('gamma', 0.95),
('learning_rate', 0.001),
('learning_starts', 1000),
('n_timesteps', 20000.0),
('normalize', True),
('policy', 'MultiInputPolicy'),
('policy_kwargs', 'dict(net_arch=[64, 64], n_critics=1)'),
('replay_buffer_class', 'HerReplayBuffer'),
('replay_buffer_kwargs',
"dict( online_sampling=True, goal_selection_strategy='future', "
'n_sampled_goal=4 )'),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
# Environment Arguments
```python
{'render': True}
```
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