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摘要翻译：
我们引入了一个具有挑战性的现实世界规划问题，其中必须在空间区域的每个位置在每个时间点采取行动。我们将林业规划作为激励应用。在大尺度时空规划问题中，状态空间和行动空间被定义为分布在城市或森林等大空间区域上的许多局部状态空间和行动空间的交叉积。这些问题具有状态不确定性，具有涉及空间约束的复杂效用函数，通常必须依赖于仿真而不是显式的转移模型。我们将LSST问题定义为强化学习问题，并提出了一种使用策略梯度的解决方案。我们比较了两种不同的策略：一种明确的策略，确定空间中的每个位置和在那里采取的行动；和一个抽象的策略，它定义了空间中所有位置要采取的行动的比例。我们证明了抽象策略比基本策略具有更强的鲁棒性，并且以更少的参数获得了更高的回报。这种抽象策略也更适合LSST问题领域的实践者为使这些方法广泛有用所需的属性。
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英文标题：
《Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal
Decision Making》
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作者：
Mark Crowley, John Nelson, David L Poole
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
We introduce a challenging real-world planning problem where actions must be taken at each location in a spatial area at each point in time. We use forestry planning as the motivating application. In Large Scale Spatial-Temporal (LSST) planning problems, the state and action spaces are defined as the cross-products of many local state and action spaces spread over a large spatial area such as a city or forest. These problems possess state uncertainty, have complex utility functions involving spatial constraints and we generally must rely on simulations rather than an explicit transition model. We define LSST problems as reinforcement learning problems and present a solution using policy gradients. We compare two different policy formulations: an explicit policy that identifies each location in space and the action to take there; and an abstract policy that defines the proportion of actions to take across all locations in space. We show that the abstract policy is more robust and achieves higher rewards with far fewer parameters than the elementary policy. This abstract policy is also a better fit to the properties that practitioners in LSST problem domains require for such methods to be widely useful.
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PDF链接：
https://arxiv.org/pdf/1205.2651
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