Distributionally Robust Strategy Synthesis for Switched Stochastic Systems

We present a novel framework for formal control of uncertain discrete-time switched stochastic systems against probabilistic reach-avoid specifications, defined as the probability of reaching a goal region while being safe. In particular, we consider stochastic systems with additive noise, whose distribution lies in an ambiguity set of distributions that are $\varepsilon-$close to a nominal one according to the Wasserstein distance. For this class of systems, we derive control synthesis algorithms that are robust against all these distributions and maximize the probability of satisfying a reach-avoid specification. The framework we present first learns an abstraction of a switched stochastic system as a robust Markov decision process (robust MDP) by accounting for both the stochasticity of the system and the uncertainty in the noise distribution. Then, it synthesizes a strategy on the resulting robust MDP that maximizes the probability of satisfying the property and is robust to all uncertainty in the system. This strategy is then refined into a switching strategy for the original stochastic system. By exploiting tools from optimal transport and stochastic programming, we show that synthesizing such a strategy reduces to solving a set of linear programs, thus guaranteeing efficiency. We experimentally validate the efficacy of our framework on various case studies, including both linear and non-linear switched stochastic systems.