Modeling of reinforced concrete structures through the use of 3D detailed models derives significant numerical issues especially when dealing with large-scale meshes that incorporate large numbers of reinforcement bars embedded in the hexahedral mesh. In 3D detailed reinforced concrete simulations, mapping the reinforcement grid inside the concrete hexahedral finite elements is performed using ...

Robots and autonomous systems that operate around humans will likely always rely on kill switches that stop their execution and allow them to be remote-controlled for the safety of humans or to prevent damage to the system. It is theoretically possible for an autonomous system with sufficient sensor and effector capability and using reinforcement learning to learn that the kill switch deprives ...

Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source policies during the learning process is of great importance yet challenging. There has been little theoretical analysis of this problem. In this paper, we develop an optimal online method to select source policies fo...

An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large amount of interactions with the environment in order to master a skill. The learned skill usually generalizes poorly across domains and re-training is often necessary when presented with a new task. We present a framework that combines methods in formal methods with hiera...

In the context of an eco-responsible production and distribution electrical energy at local scale urban territory, we consider a smart grid as system interconnecting different prosumers, which all retain their decision-making autonomy defend own interests in comprehensive where rules, accepted by all, encourage virtuous behavior. this paper, present analyze model management method for grids tha...

Most utilities across the world already have demand response (DR) programs in place to incentivise consumers reduce or shift their electricity consumption from peak periods off-peak hours usually financial incentives. With increasing electrification of vehicles, emerging technologies such as vehicle-to-grid (V2G) and vehicle-to-home (V2H) potential offer a broad range benefits services achieve ...

In the paper the results obtained from experiments at a modelled reinforced building in case of a direct lightning strike are compared with calculations. The comparison includes peak values of the magnetic field Hmax, its derivative (dH/dt)max and of induced voltages umax in typical cable routings. The experiments are performed at a 1:6 scaled building and the results are extrapolated using the...

This paper introduces a novel framework for modeling interacting humans in a multi-stage game environment by combining concepts from game theory and reinforcement learning. The proposed model has the following desirable characteristics: (1) Bounded rational players, (2) strategic (i.e., players account for one another’s reward functions), and (3) is computationally feasible even on moderately l...