论文标题
对象动态的相关性指导建模用于增强学习
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning
论文作者
论文摘要
当前的深入增强学习（RL）方法包含了有关环境的最低先验知识，从而限制了计算和样本效率。 \ textit {对象}提供了世界的简洁和因果描述，许多最近的作品提出了使用先验和损失的无监督对象表示学习，而不是视觉一致性（例如视觉一致性）。但是，对象动态和交互也是对象的关键提示。在本文中，我们提出了一个框架来推理对象动态和行为，以快速确定最小和特定于任务的对象表示。为了证明有必要对对象行为和动态进行推理，我们引入了一套RGBD Mujoco对象收集和回避任务，尽管直观且视觉上简单，令人困惑的最新无监督的对象表示学习算法。我们还使用我们的对象表示和标准RL和计划算法来强调了该框架在几个Atari游戏上的潜力，比现有的Deep RL算法更快地学习算法。
Current deep reinforcement learning (RL) approaches incorporate minimal prior knowledge about the environment, limiting computational and sample efficiency. \textit{Objects} provide a succinct and causal description of the world, and many recent works have proposed unsupervised object representation learning using priors and losses over static object properties like visual consistency. However, object dynamics and interactions are also critical cues for objectness. In this paper we propose a framework for reasoning about object dynamics and behavior to rapidly determine minimal and task-specific object representations. To demonstrate the need to reason over object behavior and dynamics, we introduce a suite of RGBD MuJoCo object collection and avoidance tasks that, while intuitive and visually simple, confound state-of-the-art unsupervised object representation learning algorithms. We also highlight the potential of this framework on several Atari games, using our object representation and standard RL and planning algorithms to learn dramatically faster than existing deep RL algorithms.
