利用合成环境进行视觉基础概念的人类式组合学习
Human-like compositional learning of visually-grounded concepts using synthetic environments
AI总结：
本文在3D合成环境中研究多模态强化学习智能体如何通过试错学习将限定词和介词等复杂概念与视觉目标组合基础化，并证明课程学习可减少15%训练回合、提升学习效率，使智能体能够分解未见指令并快速适应新视觉组合。
摘要：
语言的组合结构使人类能够分解复杂短语并将其映射到新颖的视觉概念，展现出灵活的智能。尽管已有若干算法表现出组合性，但它们未能阐明人类如何通过试错学习组合概念类别并将视觉线索进行基础化。为研究这一多模态学习挑战，我们设计了一个3D合成环境，其中智能体通过强化学习学习导航到由自然语言指令指定的目标。这些指令包含名词、属性，以及关键的限定词、介词或两者兼有。大量词汇组合提高了视觉基础任务的组合复杂性，因为当指令指定导航到“红色球体下方的一些蓝色立方体”时，导航到红色球体上方的蓝色立方体不会获得奖励。我们首先证明，强化学习智能体能够将限定词概念基础化到视觉目标，但在更复杂的介词概念上表现困难。其次，我们表明课程学习这一人类采用的策略能够提高概念学习效率，在限定词环境中将所需训练回合数减少15%，并使智能体能够轻松学习介词概念。最后，我们证实，在限定词或介词概念上训练的智能体能够分解留出测试指令，并快速调整其导航策略以适应未见过的视觉物体组合。借助合成环境，我们的研究结果表明，多模态强化学习智能体能够实现对复杂概念类别的组合理解，并凸显了类人学习策略在提高人工系统学习效率方面的有效性。
英文摘要：
The compositional structure of language enables humans to decompose complex phrases and map them to novel visual concepts, showcasing flexible intelligence. While several algorithms exhibit compositionality, they fail to elucidate how humans learn to compose concept classes and ground visual cues through trial and error. To investigate this multi-modal learning challenge, we designed a 3D synthetic environment in which an agent learns, via reinforcement, to navigate to a target specified by a natural language instruction. These instructions comprise nouns, attributes, and critically, determiners, prepositions, or both. The vast array of word combinations heightens the compositional complexity of the visual grounding task, as navigating to a blue cube above red spheres is not rewarded when the instruction specifies navigating to "some blue cubes below the red sphere". We first demonstrate that reinforcement learning agents can ground determiner concepts to visual targets but struggle with more complex prepositional concepts. Second, we show that curriculum learning, a strategy humans employ, enhances concept learning efficiency, reducing the required training episodes by 15% in determiner environments and enabling agents to easily learn prepositional concepts. Finally, we establish that agents trained on determiner or prepositional concepts can decompose held-out test instructions and rapidly adapt their navigation policies to unseen visual object combinations. Leveraging synthetic environments, our findings demonstrate that multi-modal reinforcement learning agents can achieve compositional understanding of complex concept classes and highlight the efficacy of human-like learning strategies in improving artificial systems' learning efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
