# Custom experiment for robot training

### Hyperparameter tunning (existing environment)

1. Set the desired target in the corresponding environment in gym-gazebo2 submodule.
• self.target_position
• self.target_orientation
2. Set the desired hyperparameters in the corresponding default script of the algorithm in baselines submodule.

### Create your own train script to use your own environment

1. Create a session
2. Get the hyperparameters from the corresponding defaults script of the algorithm to be used (you will need to add a new dictionary for your own environment)
3. Make the environment
• DummyVecEnv for a single instance
• SubprocVecEnv for multiple instances
4. Call the corresponding learn function of the algorithm to be used

Optional:

• Save the statistics and checkpoints in Tensorboard
• checkpoints
• tensorboard
• log
• monitor
• progress
• Save the used hyperparameters in the training

:warning: Be aware of env.set_episode_size(episode_size) function, if it is not called once the environment is made, the default size of the episode will be 1024. It is advisable to set the same value as the one to be used in the learning algorithm, or at least a power of 2.

### Create your own run script to use your own environment

1. Create a session
2. Get the hyperparameters from the corresponding defaults script of the algorithm in baselines submodule used in training time, and also the target in your own environment
• self.target_position
• self.target_orientation
3. Make a single environment (DummyVecEnv)
4. Normalize the environment (VecNormalize) if you have normalized in the training
5. Make the model and load the checkpoint you want to test
6. Get the actions from the model
• stochastic
• deterministic
7. Execute the actions

Optional:

• Save in files some useful information such us:
• Accuracy
• Position error (axes)
• Orientation error (quaternion)