  Communication is not only an action of choosing a signal, but needs to
consider the context and sensor signals. It also needs to decide what
information is communicated and how it is represented in or understood from
signals. Therefore, communication should be realized comprehensively together
with its purpose and other functions.
  The recent successful results in end-to-end reinforcement learning (RL) show
the importance of comprehensive learning and the usefulness of end-to-end RL.
Although little is known, we have shown that a variety of communications emerge
through RL using a (recurrent) neural network (NN). Here, three of them are
introduced.
  In the 1st one, negotiation to avoid conflicts among 4 randomly-picked agents
was learned. Each agent generates a binary signal from the output of its
recurrent NN (RNN), and receives 4 signals from the agents three times. After
learning, each agent made an appropriate final decision after negotiation for
any combination of 4 agents. Differentiation of individuality among the agents
also could be seen.
  The 2nd one focused on discretization of communication signal. A sender agent
perceives the receiver's location and generates a continuous signal twice by
its RNN. A receiver agent receives them sequentially, and moves according to
its RNN's output to reach the sender's location. When noises were added to the
signal, it was binarized through learning and 2-bit communication was
established.
  The 3rd one focused on end-to-end comprehensive communication. A sender
receives 1,785-pixel real camera image on which a real robot can be seen, and
sends two sounds whose frequencies are computed by its NN. A receiver receives
them, and two motion commands for the robot are generated by its NN. After
learning, though some preliminary learning was necessary for the sender, the
robot could reach the goal from any initial location.
