Robot control for tasks such as moving around obstacles or gras** objects has advanced
significantly in the last few decades. However, controlling robots to perform complex tasks is …

The design and control of autonomous systems that operate in uncertain or adversarial
environments can be facilitated by formal modeling and analysis. Probabilistic model …

We present the probabilistic model checker Storm. Storm supports the analysis of discrete-
and continuous-time variants of both Markov chains and Markov decision processes. Storm …

We launch the new probabilistic model checker S torm. It features the analysis of discrete-
and continuous-time variants of both Markov chains and MDPs. It supports the P rism and …

Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …

We present a reinforcement learning (RL) frame-work to synthesize a control policy from a
given linear temporal logic (LTL) specification in an unknown stochastic environment that …

A reinforcement learning-based adaptive energy management (RLAEM) is proposed for a
hybrid electric tracked vehicle (HETV) in this paper. A control oriented model of the HETV is …

We present a model-free reinforcement learning algorithm to synthesize control policies that
maximize the probability of satisfying high-level control objectives given as Linear Temporal …

We provide the first solution for model-free reinforcement learning of ω-regular objectives for
Markov decision processes (MDPs). We present a constructive reduction from the almost …

This paper addresses the problem of learning optimal policies for satisfying signal temporal
logic (STL) specifications by agents with unknown stochastic dynamics. The system is …