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 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 …
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 …
Stochastic hybrid systems have received significant attentions as a relevant modeling framework describing many systems, from engineering to the life sciences: they enable the …
This paper concerns the efficient construction of a safety shield for reinforcement learning. We specifically target scenarios that incorporate uncertainty and use Markov decision …
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 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 …
T Liu, Y Zou, D Liu, F Sun - IEEE Transactions on Industrial …, 2015 - ieeexplore.ieee.org
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 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 …