Abstract
This paper presents a framework for learning state and action abstractions in sequential decision-making domains. Our framework, planning abstraction from language (PARL), utilizes language-annotated demonstrations to automatically discover a symbolic and abstract action space and induce a latent state abstraction based on it. PARL consists of three stages: 1) recovering object-level and action concepts, 2) learning state abstractions, abstract action feasibility, and transition models, and 3) applying low-level policies for abstract actions. During inference, given the task description, PARL first makes abstract action plans using the latent transition and feasibility functions, then refines the high-level plan using low-level policies. PARL generalizes across scenarios involving novel object instances and environments, unseen concept compositions, and tasks that require longer planning horizons than settings it is trained on.
Abstract (translated)
本文提出了一种在序列决策领域学习状态和动作抽象的框架。我们的框架规划抽象语言 (PARL)，利用语言注释的演示来自动发现符号和抽象动作空间，并基于它诱导潜在状态抽象。PARL包括三个阶段：1）恢复物体级和动作概念，2）学习状态抽象、抽象动作可行性以及转移模型，3）应用低级策略对抽象动作进行规划。在推理过程中，给定任务描述，PARL首先使用潜在转移和可行性函数进行抽象动作计划，然后通过低级策略优化高级计划。PARL在涉及新颖物体实例和环境的场景、未见过的概念组合以及需要比其训练环境更长的规划视野的任务中具有泛化性。
URL
https://arxiv.org/abs/2405.03864
