以对象为中心的世界模型与蒙特卡洛树搜索
Object-Centric World Models Meet Monte Carlo Tree Search
AI总结：
本文提出ObjectZero算法，通过对象中心表示和图神经网络，结合蒙特卡洛树搜索实现更有效的环境建模与强化学习。
摘要：
在本文中，我们介绍了ObjectZero，一种新颖的强化学习（RL）算法，利用对象级表示来更有效地建模动态环境。与传统方法将世界视为单一未分化输入不同，我们的方法采用图神经网络（GNNs）来捕捉多个对象之间的复杂相互作用。这些可以被操作和相互作用的对象构成了我们模型对环境理解的基础。我们在一个充满多样、互动对象的复杂环境中训练该算法，展示了其有效学习和预测对象动态的能力。我们的结果表明，一个基于对象中心表示的结构化世界模型可以成功整合到一种基于模型的RL算法中，该算法利用蒙特卡洛树搜索作为规划模块。
英文摘要：
In this paper, we introduce ObjectZero, a novel reinforcement learning (RL) algorithm that leverages the power of object-level representations to model dynamic environments more effectively. Unlike traditional approaches that process the world as a single undifferentiated input, our method employs Graph Neural Networks (GNNs) to capture intricate interactions among multiple objects. These objects, which can be manipulated and interact with each other, serve as the foundation for our model's understanding of the environment. We trained the algorithm in a complex setting teeming with diverse, interactive objects, demonstrating its ability to effectively learn and predict object dynamics. Our results highlight that a structured world model operating on object-centric representations can be successfully integrated into a model-based RL algorithm utilizing Monte Carlo Tree Search as a planning module.
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。
