Hartmanns, Arnd

Niehage, Mathis

Remke, Anne

English

We apply reinforcement learning to approximate the optimal probability that a stochastic hybrid system satisfies a temporal logic formula. We consider systems with (non)linear continuous dynamics, random events following general continuous probability distributions, and discrete nondeterministic choices. We present a discretized view of states to the learner, but simulate the continuous system. Once we have learned a near-optimal scheduler resolving the choices, we use statistical model checking to estimate its probability of satisfying the formula. We implemented the approach using Q-learning in the tools HYPEG and modes, which support Petri net- and hybrid automata-based models, respectively. Via two case studies, we show the feasibility of the approach, and compare its performance and effectiveness to existing analytical techniques for a linear model. We find that our new approach quickly finds near-optimal prophetic as well as non-prophetic schedulers, which maximize or minimize the probability that a specific signal temporal logic property is satisfied

University of Twente Research Information

Learning optimal decisions for stochastic hybrid systems

https://ris.utwente.nl/ws/files/275756293/3487212.3487339.pdf

Learning optimal decisions for stochastic hybrid systems

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University of Twente Research Information