@inproceedings{8215b9e2fec048ab9b02b61d0058b401,
title = "Teaching Stratego to Play Ball: Optimal Synthesis for Continuous Space MDPs",
abstract = "Formal models of cyber-physical systems, such as priced timed Markov decision processes, require a state space with continuous and discrete components. The problem of controller synthesis for such systems then can be cast as finding optimal strategies for Markov decision processes over a Euclidean state space. We develop two different reinforcement learning strategies that tackle the problem of continuous state spaces via online partition refinement techniques. We provide theoretical insights into the convergence of partition refinement schemes. Our techniques are implemented in Open image in new window . Experimental results show the advantages of our new techniques over previous optimization algorithms of Open image in new window .",
author = "Manfred Jaeger and Jensen, {Peter Gj{\o}l} and Larsen, {Kim Guldstrand} and Legay, {Axel Bernard E} and Sean Sedwards and Taankvist, {Jakob Haahr}",
year = "2019",
month = oct,
day = "28",
doi = "10.1007/978-3-030-31784-3_5",
language = "English",
isbn = "978-3-030-31783-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "81--97",
editor = "Yu-Fang Chen and Chih-Hong Cheng and Javier Esparza",
booktitle = "Automated Technology for Verification and Analysis- 17th International Symposium, AVTA 2019, Proceedings",
address = "Germany",
note = "International Symposium on Automated Technology for Verification and Analysis, ATVA ; Conference date: 28-10-2019 Through 31-10-2019",
}