  Robotics research has been focusing on cooperative multi-agent problems,
where agents must work together and communicate to achieve a shared objective.
To tackle this challenge, we explore imitation learning algorithms. These
methods learn a controller by observing demonstrations of an expert, such as
the behaviour of a centralised omniscient controller, which can perceive the
entire environment, including the state and observations of all agents.
  Performing tasks with complete knowledge of the state of a system is
relatively easy, but centralised solutions might not be feasible in real
scenarios since agents do not have direct access to the state but only to their
observations. To overcome this issue, we train end-to-end Neural Networks that
take as input local observations obtained from an omniscient centralised
controller, i.e., the agents' sensor readings and the communications received,
producing as output the action to be performed and the communication to be
transmitted.
  This study concentrates on two cooperative tasks using a distributed
controller: distributing the robots evenly in space and colouring them based on
their position relative to others. While an explicit exchange of messages
between the agents is required to solve the second task, in the first one, a
communication protocol is unnecessary, although it may increase performance.
  The experiments are run in Enki, a high-performance open-source simulator for
planar robots, which provides collision detection and limited physics support
for robots evolving on a flat surface. Moreover, it can simulate groups of
robots hundreds of times faster than real-time.
  The results show how applying a communication strategy improves the
performance of the distributed model, letting it decide which actions to take
almost as precisely and quickly as the expert controller.
