  The agent program, called Samu, is an experiment to build a disembodied
DevRob (Developmental Robotics) chatter bot that can talk in a natural language
like humans do. One of the main design feature is that Samu can be interacted
with using only a character terminal. This is important not only for practical
aspects of Turing test or Loebner prize, but also for the study of basic
principles of Developmental Robotics. Our purpose is to create a rapid
prototype of Q-learning with neural network approximators for Samu. We sketch
out the early stages of the development process of this prototype, where Samu's
task is to predict the next sentence of tales or conversations. The basic
objective of this paper is to reach the same results using reinforcement
learning with general function approximators that can be achieved by using the
classical Q lookup table on small input samples. The paper is closed by an
experiment that shows a significant improvement in Samu's learning when using
LZW tree to narrow the number of possible Q-actions.
