Although reinforcement learning has seen remarkable progress over the last
years, solving robust dexterous object-manipulation tasks in multi-object
settings remains a challenge. In this paper, we focus on models that can learn
manipulation tasks in fixed multi-object settings and extrapolate this skill
zero-shot without any drop in performance when the number of objects changes.
We consider the generic task of bringing a specific cube out of a set to a goal
position. We find that previous approaches, which primarily leverage attention
and graph neural network-based architectures, do not generalize their skills
when the number of input objects changes while scaling as $K^2$. We propose an
alternative plug-and-play module based on relational inductive biases to
overcome these limitations. Besides exceeding performances in their training
environment, we show that our approach, which scales linearly in $K$, allows
agents to extrapolate and generalize zero-shot to any new object number.

本文提出一种基于关系归纳偏见的新型插入式模块，可使代理程序在学习固定的多对象设置中学习操作任务，并在输入对象数量改变时零样本泛化，解决了前人方法因其复杂度而无法泛化的问题。

利用线性关系网络进行组合多对象强化学习

Compositional Multi-Object Reinforcement Learning with Linear Relation  Networks

Recent research has highlighted the role of relational inductive biases in
building learning agents that can generalize and reason in a compositional
manner. However, while relational learning algorithms such as graph neural
networks (GNNs) show promise, we do not understand how effectively these
approaches can adapt to new tasks. In this work, we study the task of logical
generalization using GNNs by designing a benchmark suite grounded in
first-order logic. Our benchmark suite, GraphLog, requires that learning
algorithms perform rule induction in different synthetic logics, represented as
knowledge graphs. GraphLog consists of relation prediction tasks on 57 distinct
logical domains. We use GraphLog to evaluate GNNs in three different setups:
single-task supervised learning, multi-task pretraining, and continual
learning. Unlike previous benchmarks, our approach allows us to precisely
control the logical relationship between the different tasks. We find that the
ability for models to generalize and adapt is strongly determined by the
diversity of the logical rules they encounter during training, and our results
highlight new challenges for the design of GNN models. We publicly release the
dataset and code used to generate and interact with the dataset at
this https URL

本研究探讨使用关系学习算法如何进行逻辑推理，在设计了一套基于一阶逻辑的基准测试套件 GraphLog 后，我们使用图神经网络 (GNN) 来进行评估，并发现模型的泛化和适应能力强度受到训练中遇到的不同逻辑规则的多样性的影响。

图神经网络中的逻辑推理能力评估

Evaluating Logical Generalization in Graph Neural Networks

Artificial intelligence (AI) has undergone a renaissance recently, making
major progress in key domains such as vision, language, control, and
decision-making. This has been due, in part, to cheap data and cheap compute
resources, which have fit the natural strengths of deep learning. However, many
defining characteristics of human intelligence, which developed under much
different pressures, remain out of reach for current approaches. In particular,
generalizing beyond one's experiences--a hallmark of human intelligence from
infancy--remains a formidable challenge for modern AI.
The following is part position paper, part review, and part unification. We
argue that combinatorial generalization must be a top priority for AI to
achieve human-like abilities, and that structured representations and
computations are key to realizing this objective. Just as biology uses nature
and nurture cooperatively, we reject the false choice between
"hand-engineering" and "end-to-end" learning, and instead advocate for an
approach which benefits from their complementary strengths. We explore how
using relational inductive biases within deep learning architectures can
facilitate learning about entities, relations, and rules for composing them. We
present a new building block for the AI toolkit with a strong relational
inductive bias--the graph network--which generalizes and extends various
approaches for neural networks that operate on graphs, and provides a
straightforward interface for manipulating structured knowledge and producing
structured behaviors. We discuss how graph networks can support relational
reasoning and combinatorial generalization, laying the foundation for more
sophisticated, interpretable, and flexible patterns of reasoning. As a
companion to this paper, we have released an open-source software library for
building graph networks, with demonstrations of how to use them in practice.

本文提出结构化表示法和计算方法是实现人工智能与人类智能相似的能力的关键，通过使用深度学习体系结构内的关系感应偏差，介绍一种新的人工智能工具箱构建模块 -- 图形网络，它支持关系推理和组合推广，为推理提供更复杂、可解释和灵活的模式。