This paper investigates the idea of encoding object-centered representations
in the design of the reward function and policy architectures of a
language-guided reinforcement learning agent. This is done using a combination
of object-wise permutation invariant networks inspired from Deep Sets and
gated-attention mechanisms. In a 2D procedurally-generated world where agents
targeting goals in natural language navigate and interact with objects, we show
that these architectures demonstrate strong generalization capacities to
out-of-distribution goals. We study the generalization to varying numbers of
objects at test time and further extend the object-centered architectures to
goals involving relational reasoning.

本文研究了在自然语言引导下的强化学习中，将以对象为中心的表现编码到奖励函数和策略架构中的想法。通过使用受深度集合启发的对象排列不变网络和门控注意机制的组合，我们在二维过程生成的世界中显示出这些结构对于分布外的目标具有强大的泛化能力，同时我们研究了在测试时对象数量的泛化和将以对象为中心的架构扩展到涉及关系推理的目标。