Automated verification and synthesis of stochastic hybrid systems: A survey
Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …
framework describing many systems, from engineering to the life sciences: they enable the …
Formal synthesis of stochastic systems via control barrier certificates
This article focuses on synthesizing control policies for discrete-time stochastic control
systems together with a lower bound on the probability that the systems satisfy the complex …
systems together with a lower bound on the probability that the systems satisfy the complex …
Formal controller synthesis for continuous-space MDPs via model-free reinforcement learning
A novel reinforcement learning scheme to synthesize policies for continuous-space Markov
decision processes (MDPs) is proposed. This scheme enables one to apply model-free, off …
decision processes (MDPs) is proposed. This scheme enables one to apply model-free, off …
Compositional abstraction-based synthesis for networks of stochastic switched systems
In this paper, we provide a compositional approach for constructing finite abstractions (aka
finite Markov decision processes (MDPs)) of interconnected discrete-time stochastic …
finite Markov decision processes (MDPs)) of interconnected discrete-time stochastic …
Robust dynamic programming for temporal logic control of stochastic systems
S Haesaert, S Soudjani - IEEE Transactions on Automatic …, 2020 - ieeexplore.ieee.org
Discrete-time stochastic systems are an essential modeling tool for many engineering
systems. We consider stochastic control systems that are evolving over continuous spaces …
systems. We consider stochastic control systems that are evolving over continuous spaces …
Translating omega-regular specifications to average objectives for model-free reinforcement learning
Recent success in reinforcement learning (RL) has brought renewed attention to the design
of reward functions by which agent behavior is reinforced or deterred. Manually designing …
of reward functions by which agent behavior is reinforced or deterred. Manually designing …
Formal policy synthesis for continuous-state systems via reinforcement learning
M Kazemi, S Soudjani - … Methods: 16th International Conference, IFM 2020 …, 2020 - Springer
This paper studies satisfaction of temporal properties on unknown stochastic processes that
have continuous state spaces. We show how reinforcement learning (RL) can be applied for …
have continuous state spaces. We show how reinforcement learning (RL) can be applied for …
SySCoRe: Synthesis via stochastic coupling relations
We present SySCoRe, a MATLAB toolbox that synthesizes controllers for stochastic
continuous-state systems to satisfy temporal logic specifications. Starting from a system …
continuous-state systems to satisfy temporal logic specifications. Starting from a system …
Compositional synthesis of control barrier certificates for networks of stochastic systems against ω-regular specifications
This paper is concerned with a compositional scheme for the construction of control barrier
certificates for interconnected discrete-time stochastic systems. The main objective is to …
certificates for interconnected discrete-time stochastic systems. The main objective is to …
Data-driven memory-dependent abstractions of dynamical systems
We propose a sample-based, sequential method to abstract a (potentially black-box)
dynamical system with a sequence of memory-dependent Markov chains of increasing size …
dynamical system with a sequence of memory-dependent Markov chains of increasing size …