This work introduces a neuro-symbolic agent that combines deep reinforcement
learning (DRL) with temporal logic (TL) to achieve systematic zero-shot, i.e.,
never-seen-before, generalisation of formally specified instructions. In
particular, we present a neuro-symbolic framework where a symbolic module
transforms TL specifications into a form that helps the training of a DRL agent
targeting generalisation, while a neural module learns systematically to solve
the given tasks. We study the emergence of systematic learning in different
settings and find that the architecture of the convolutional layers is key when
generalising to new instructions. We also provide evidence that systematic
learning can emerge with abstract operators such as negation when learning from
a few training examples, which previous research have struggled with.

介绍了一种将深度强化学习与时间逻辑相结合的神经符号代理，以实现形式化规定指令的系统性零射 (就是指未曾见过的情况)。研究证明，卷积层的架构在泛化新指令时发挥了关键作用，并证明通过学习少量训练样例，可以从抽象操作符中实现系统化的学习。