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.

研究将最近开发的 BabyAI 网格世界平台转变为发送者 / 接收者设置，以测试深层强化学习技术是否足以激励建立通用代理之间的基于地面的离散通信协议的假设。结果表明，适当的环境激励确实可以避免测量或特殊归纳偏差的情况，而且更长的通信间隔激励了更抽象的语义。一些情况下，通讯代理比一个单一的代理更快地适应了新环境，展示了新兴通信对于迁移学习和泛化的潜力。