This paper introduces Relation Networks (RNs), a simple and effective module for relational reasoning in neural networks. The authors demonstrate that RNs can be used as a plug-and-play component to solve tasks that require relational reasoning, such as visual question answering, text-based question answering, and reasoning about dynamic physical systems. RNs are designed to focus explicitly on relational reasoning, and they can be integrated with other neural network architectures like CNNs and LSTMs to produce implicit object-like representations that enable relational reasoning. The authors tested RNs on three tasks: visual question answering using the CLEVR dataset, text-based question answering using the bAbI dataset, and reasoning about dynamic physical systems. On the CLEVR dataset, RNs achieved state-of-the-art, super-human performance, outperforming other models. On the bAbI dataset, RNs solved 18 out of 20 tasks. In the dynamic physical systems task, RNs were able to infer connections between objects and count the number of connected systems. The authors also show that powerful convolutional networks are not general enough to solve relational questions, but can gain this capacity when augmented with RNs. RNs are data-efficient and operate on a set of objects in a manner that is order-invariant. They are able to learn to infer relations, and they can be conditioned on question embeddings to improve performance. The paper concludes that RNs are a powerful and flexible approach for learning to perform rich, structured reasoning in complex, real-world domains. They can be applied to a wide range of tasks that require relational reasoning, and they are particularly effective in tasks that require reasoning about the relationships between objects.This paper introduces Relation Networks (RNs), a simple and effective module for relational reasoning in neural networks. The authors demonstrate that RNs can be used as a plug-and-play component to solve tasks that require relational reasoning, such as visual question answering, text-based question answering, and reasoning about dynamic physical systems. RNs are designed to focus explicitly on relational reasoning, and they can be integrated with other neural network architectures like CNNs and LSTMs to produce implicit object-like representations that enable relational reasoning. The authors tested RNs on three tasks: visual question answering using the CLEVR dataset, text-based question answering using the bAbI dataset, and reasoning about dynamic physical systems. On the CLEVR dataset, RNs achieved state-of-the-art, super-human performance, outperforming other models. On the bAbI dataset, RNs solved 18 out of 20 tasks. In the dynamic physical systems task, RNs were able to infer connections between objects and count the number of connected systems. The authors also show that powerful convolutional networks are not general enough to solve relational questions, but can gain this capacity when augmented with RNs. RNs are data-efficient and operate on a set of objects in a manner that is order-invariant. They are able to learn to infer relations, and they can be conditioned on question embeddings to improve performance. The paper concludes that RNs are a powerful and flexible approach for learning to perform rich, structured reasoning in complex, real-world domains. They can be applied to a wide range of tasks that require relational reasoning, and they are particularly effective in tasks that require reasoning about the relationships between objects.