  Recently, triggered by the impressive results in TV-games or game of Go by
Google DeepMind, end-to-end reinforcement learning (RL) is collecting
attentions. Although little is known, the author's group has propounded this
framework for around 20 years and already has shown various functions that
emerge in a neural network (NN) through RL. In this paper, they are introduced
again at this timing.
  "Function Modularization" approach is deeply penetrated subconsciously. The
inputs and outputs for a learning system can be raw sensor signals and motor
commands. "State space" or "action space" generally used in RL show the
existence of functional modules. That has limited reinforcement learning to
learning only for the action-planning module. In order to extend reinforcement
learning to learning of the entire function on a huge degree of freedom of a
massively parallel learning system and to explain or develop human-like
intelligence, the author has believed that end-to-end RL from sensors to motors
using a recurrent NN (RNN) becomes an essential key. Especially in the higher
functions, this approach is very effective by being free from the need to
decide their inputs and outputs.
  The functions that emerge, we have confirmed, through RL using a NN cover a
broad range from real robot learning with raw camera pixel inputs to
acquisition of dynamic functions in a RNN. Those are (1)image recognition,
(2)color constancy (optical illusion), (3)sensor motion (active recognition),
(4)hand-eye coordination and hand reaching movement, (5)explanation of brain
activities, (6)communication, (7)knowledge transfer, (8)memory, (9)selective
attention, (10)prediction, (11)exploration. The end-to-end RL enables the
emergence of very flexible comprehensive functions that consider many things in
parallel although it is difficult to give the boundary of each function
clearly.
