Generalizing Emergent Communication
  We converted the recently developed BabyAI grid world platform to a
sender/receiver setup in order to test the hypothesis that established deep
reinforcement learning techniques are sufficient to incentivize the emergence
of a grounded discrete communication protocol between generalized agents. This
is in contrast to previous experiments that employed straight-through
estimation or specialized inductive biases. Our results show that these can
indeed be avoided, by instead providing proper environmental incentives.
Moreover, they show that a longer interval between communications incentivized
more abstract semantics. In some cases, the communicating agents adapted to new
environments more quickly than a monolithic agent, showcasing the potential of
emergent communication for transfer learning and generalization in general.
