论文标题
学习和计划的局限性：最小的足够信息过渡系统
The Limits of Learning and Planning: Minimal Sufficient Information Transition Systems
论文作者
论文摘要
在本文中，我们将策略或计划视为一个过渡系统，这是在信息状态的空间状态下，基于有限的感应，记忆，计算和驱动，反映了机器人或其他观察者的观点。无论是通过学习算法，计划算法还是人类洞察力获得的政策，我们都希望知道给定机器人硬件和任务的可行性限制。为了寻求找到最佳的政策，我们在一般环境中确定，最小信息过渡系统（ITS）的存在至合理的等效假设，并且在某些一般条件下是独一无二的。然后，我们将理论应用于几个问题，包括最佳传感器融合/过滤，解决基本计划任务以及为可行策略找到最小的表示形式。
In this paper, we view a policy or plan as a transition system over a space of information states that reflect a robot's or other observer's perspective based on limited sensing, memory, computation, and actuation. Regardless of whether policies are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. Toward the quest to find the best policies, we establish in a general setting that minimal information transition systems (ITSs) exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for feasible policies.
