README.md

---
library_name: stable-baselines3
tags:
- FetchPickAndPlaceDense-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: FetchPickAndPlaceDense-v4
      type: FetchPickAndPlaceDense-v4
    metrics:
    - type: mean_reward
      value: -2.07 +/- 1.16
      name: mean_reward
      verified: false
license: mit
language:
- en
---

# **TQC** Agent playing **FetchPickAndPlaceDense-v4**
- [Github Repository](https://github.com/kuds/rl-fetch)
- [Google Colab Notebook](https://colab.research.google.com/github/kuds/rl-fetch/blob/main/Fetch/Pick%20and%20Place/%5BFetch%20Pick%20%26%20Place%5D%20Truncated%20Quantile%20Critics%20(TQC).ipynb)
- [Finding Theta - Blog Post](https://www.findingtheta.com/blog/mastering-robotic-manipulation-with-reinforcement-learning-tqc-and-ddpg-for-fetch-environments)


Then, you can load the model using the following Python code:

```python
import gymnasium as gym
from sb3_contrib import TQC
from stable_baselines3.common.env_util import make_vec_env

gymnasium.register_envs(gymnasium_robotics)

# Load the trained model
model = TQC.load("best-model.zip")

# Create the environment
env = make_vec_env("FetchPickAndPlaceDense-v4", n_envs=1)

# Reset the environment
obs, info = env.reset()

# Enjoy the trained agent
for _ in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, rewards, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, info = env.reset()
    env.render()
env.close()
```

### Hugging Face Hub

You can also use the Hugging Face Hub to load the model. First, you need to install the Hugging Face Hub library:

```bash
pip install huggingface_hub
```

Then, you can load the model from the hub using the following code:

```python
from huggingface_hub import hf_hub_download
import torch as th
from sb3_contrib import TQC
from stable_baselines3.common.env_util import make_vec_env

gymnasium.register_envs(gymnasium_robotics)

# Download the model from the Hub
model_path = hf_hub_download(repo_id="kuds/fetch-pick-place-dense-tqc", filename="best-model.zip")

# Load the model
model = TQC.load(model_path)

# Create the environment
env = make_vec_env("FetchPickAndPlaceDense-v4", n_envs=1)

# Enjoy the trained agent
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, rewards, dones, info = env.step(action)
    env.render("human")
```