Closed-Form Solutions to a Subclass of Continuous Stochastic Games via Symbolic Dynamic Programming
Abstract
Zero-sum stochastic games provide a formal-ism to study competitive sequential interactions between two agents with diametrically oppos-ing goals and evolving state. A solution to such games with discrete state was presented by Littman (Littman, 1994). The continuous state version of this game remains unsolved. In many instances continuous state solutions require non-linear optimisation, a problem for which closed-form solutions are generally unavailable. We present an exact closed-form solution to a sub-class of zero-sum continuous stochastic games that can be solved as a parameterised linear pro-gram by utilising symbolic dynamic program-ming. This novel technique is applied to calcu-late exact solutions to a variety of zero-sum con-tinuous state stochastic games. 1
Cite
Text
Kinathil et al. "Closed-Form Solutions to a Subclass of Continuous Stochastic Games via Symbolic Dynamic Programming." Conference on Uncertainty in Artificial Intelligence, 2014.Markdown
[Kinathil et al. "Closed-Form Solutions to a Subclass of Continuous Stochastic Games via Symbolic Dynamic Programming." Conference on Uncertainty in Artificial Intelligence, 2014.](https://mlanthology.org/uai/2014/kinathil2014uai-closed/)BibTeX
@inproceedings{kinathil2014uai-closed,
title = {{Closed-Form Solutions to a Subclass of Continuous Stochastic Games via Symbolic Dynamic Programming}},
author = {Kinathil, Shamin and Sanner, Scott and Della Penna, Nicolás},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2014},
pages = {390-399},
url = {https://mlanthology.org/uai/2014/kinathil2014uai-closed/}
}