Solving Relational MDPs with Exogenous Events and Additive Rewards
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.
Cite
Text
Joshi et al. "Solving Relational MDPs with Exogenous Events and Additive Rewards." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40988-2_12Markdown
[Joshi et al. "Solving Relational MDPs with Exogenous Events and Additive Rewards." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/joshi2013ecmlpkdd-solving/) doi:10.1007/978-3-642-40988-2_12BibTeX
@inproceedings{joshi2013ecmlpkdd-solving,
title = {{Solving Relational MDPs with Exogenous Events and Additive Rewards}},
author = {Joshi, Saket and Khardon, Roni and Tadepalli, Prasad and Raghavan, Aswin and Fern, Alan},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {178-193},
doi = {10.1007/978-3-642-40988-2_12},
url = {https://mlanthology.org/ecmlpkdd/2013/joshi2013ecmlpkdd-solving/}
}