Tue 18 Jan 2022 09:00 - 09:30 at Salon I - Synthesis Chair(s): Viktor Kunčak

Randomization is a powerful technique to create robust controllers, in particular in partially observable settings. The degrees of randomization have a significant impact on the system performance, yet they are intricate to get right. The use of synthesis algorithms for parametric Markov chains (pMCs) is a promising direction to support the design process of such controllers. This paper shows how to define and evaluate gradients of pMCs. Furthermore, it investigates varieties of gradient descent techniques from the machine learning community to synthesize the probabilities in a pMC. The resulting method scales to significantly larger pMCs than before and empirically outperforms the state-of-the-art, often by at least one order of magnitude.

Tue 18 Jan

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09:00 - 10:00
SynthesisVMCAI at Salon I
Chair(s): Viktor Kunčak EPFL, Switzerland
09:00
30m
Paper
Gradient-Descent for Randomized Controllers under Partial ObservabilityInPerson
VMCAI
Jip Spel RWTH Aachen University, Linus Heck RWTH Aachen University, Sebastian Junges University of California, Berkeley, Joshua Moerman Open University of the Netherlands, Joost-Pieter Katoen RWTH Aachen University
09:30
30m
Paper
Satisfiability and Synthesis Modulo OraclesRemote
VMCAI
Elizabeth Polgreen University of Edinburgh, Andrew Reynolds University of Iowa, Sanjit Seshia UC Berkeley