We formalize a simple but natural subclass of service domains for relational planning problems with object-centered, independent exogenous events and additive rewards capturing, for example, problems in inventory control. Focusing on this subclass, we present a new symbolic planning algorithm which is the first algorithm that has explicit performance guarantees for relational MDPs with exogenous events. In particular, under some technical conditions, our planning algorithm provides a monotonic lower bound on the optimal value function. To support this algorithm we present novel evaluation and reduction techniques for generalized first order decision diagrams, a knowledge representation for real-valued functions over relational world states. Our planning algorithm uses a set of focus states, which serves as a training set, to simplify and approximate the symbolic solution, and can thus be seen to perform learning for planning. A preliminary experimental evaluation demonstrates the validity of our approach.

本文介绍了一种符号规划算法，该算法是面向以对象为中心、独立的外部事件和捕捉库存控制等问题的服务域的，该算法具有明确的关于外部事件的关系MDP的性能保证，在某些技术条件下，我们的规划算法提供了关于最优值函数的单调下界。为了支持这种算法，我们使用广义一阶决策图作为知识表示，提出了新颖的评估和约简技术。我们的规划算法使用一组焦点状态，这组状态作为一个训练集，简化和近似符号解，并且可以被视为执行规划的学习。一项初步的实验评估证明了这种方法的有效性。

使用外部事件和可累计奖励解决关系型MDPs问题