Environment setup + train loop (#5)

Mujoco environment setup (vectorized on GPU) + training loop + simulate script

docs/api/train_simulate.md

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+# Training and Simulation for Brittle Star Models
+
+## Training a model
+
+To train a model, you can use the `train.py` script. This script allows to pass some parameters to customize the training process:
+
+- `--out`: The output path where the trained model will be saved.
+- `--model_type`: The type of model to train (e.g., `random`, ...)
+- `--task`: The task to train on (e.g., `directed_locomotion`, ...)
+- `--seed`: The random seed for reproducibility.
+- `--epochs`: The number of epochs to train for.
+
+This will then train the specified model on the specified task for the given number of epochs and save the trained model to the specified output path.
+
+```bash
+python train.py --out artifacts/my_model --model-type random --task directed_locomotion --seed 0 --epochs 50 
+```
+
+## Simulating a model
+
+In order to simulate and view the behavior of a trained model, you can use the `simulate.py` script. This script allows you to specify the path to a trained model and will launch a simulation using that model. This script has the following parameters:
+
+- `--model`: The path to the trained model artifact to simulate.
+- `--model-type`: The type of model to simulate (e.g., `random`, ...)
+- `--task`: The task to simulate (e.g., `directed_locomotion`, ...)
+- `--seed`: The random seed for reproducibility.
+
+```bash
+python simulate.py --model artifacts/my_model --model-type random --task directed_locomotion --seed 0
+```