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arXiv · 1306.6302

Solving Relational MDPs with Exogenous Events and Additive Rewards

S. Joshi·R. Khardon·P. Tadepalli·A. Raghavan·A. Fern
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Abstract

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.

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S. Joshi, R. Khardon, P. Tadepalli, A. Raghavan, A. Fern. 2013-06-26. Solving Relational MDPs with Exogenous Events and Additive Rewards. https://arxiv.org/abs/1306.6302

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