  We present an introspective framework inspired by the process of how humans
perform introspection. Our working assumption is that neural network
activations encode information, and building internal states from these
activations can improve the performance of an actor-critic model. We perform
experiments where we first train a Variational Autoencoder model to reconstruct
the activations of a feature extraction network and use the latent space to
improve the performance of an actor-critic when deciding which low-level
robotic behaviour to execute. We show that internal states reduce the number of
episodes needed by about 1300 episodes while training an actor-critic, denoting
faster convergence to get a high success value while completing a robotic task.
