We provide a study of how induced model sparsity can help achieve compositional generalization and better sample efficiency in grounded language learning problems. We consider simple language-conditioned navigation problems in a grid world environment with disentangled observations. We show that standard neural architectures do not always yield compositional generalization. To address this, we design an agent that contains a goal identification module that encourages sparse correlations between words in the instruction and attributes of objects, composing them together to find the goal. The output of the goal identification module is the input to a value iteration network planner. Our agent maintains a high level of performance on goals containing novel combinations of properties even when learning from a handful of demonstrations. We examine the internal representations of our agent and find the correct correspondences between words in its dictionary and attributes in the environment.

翻译：为了解决这个问题,我们设计了一个工具,其中含有一个目标识别模块,鼓励在对象的指令和属性中的文字之间少许关联,将其组合起来,以寻找目标。目标识别模块的输出是向价值循环网络规划师的投入。我们的代理商在包含新型属性组合的目标上保持高水平的绩效,即使从一些演示中学习,也包含新颖的属性组合。我们检查了我们的代理商的内部表现,发现字典中的文字和环境属性之间的正确对应关系。