# TEG
TEG is a straightforward environment for Reinforcement Learning that enables
the training of RL agents for a robot manipulator. It's based on the [Gymnasium](https://github.com/Farama-Foundation/Gymnasium)
and [Mujoco](https://github.com/deepmind/mujoco).
## Installation
This project use python 3.7+
You can install it by using pip
```bash
pip install TEG
```
Or manually cloning the github repository
```bash
git clone https://github.com/Alexfm101/TEG.git
cd TEG
python -m pip install -e .
```
## Example
TEG environment are simple Python `env` classes to allow an AI agent to interact
with them very simple. Here's an example:
```python
from TEG.envs.UR5_v0 import UR5Env_v0
env = UR5Env_v0(simulation_frames=5, torque_control= 0.01, distance_threshold=0.05)
def main():
for episode in range(5):
print("episode {}".format(episode))
env.reset()
for t in range(1000):
action = env.action_space.sample()
observation, reward, done, info = env.step(action)
if done:
print("Episode finished after {} timesteps".format(t+1))
break
return env.robot, env.sim
if __name__ == '__main__':
main()
```
## 🧾 License
The Apache 2.0 License
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