Step back with the implementation. Coded another idea, from extraction of state, a, r, state_, a basic idea of the reward(to be improved). Deletd the get_states...

Code a class QManager, to init model, data, and dqn parameters, all in DQN_loop.py, and added the option dqn_train, in model add the option to extract the atention vectors. To check fit as states.

On prediction.py, use the validate_on_data to create the funtion _states_data, wich return the states representation on the same way as the translation funtion

Use the funtion translate as template to create the get_states funtion. Like the translate funtion. As a first approach to generate the states, we will use later on the DQN program