  Consider a collaborative task carried out by two autonomous agents that are
able to communicate over a noisy channel. Each agent is only aware of its own
state, while the accomplishment of the task depends on the value of the joint
state of both agents. As an example, both agents must simultaneously reach a
certain location of the environment, while only being aware of their respective
positions. Assuming the presence of feedback in the form of a common reward to
the agents, a conventional approach would apply separately: (i) an
off-the-shelf coding and decoding scheme in order to enhance the reliability of
the communication of the state of one agent to the other; and (ii) a standard
multi-agent reinforcement learning strategy to learn how to act in the
resulting environment. In this work, it is demonstrated that the performance of
the collaborative task can be improved if the agents learn jointly how to
communicate and to act, even in the presence of a delay in the communication
channel.
