In the context of Mobile Augmented Reality, satisfying the challenging users’ requirements about Quality of Service and Quality of Experience is not an easy task due to the limited computing capabilities of mobile devices, and the rapid, free movement of users within the environment. To deal with these issues, graphical computations are typically offloaded from mobile devices to edge servers. While traditional offloading strategies rely on static optimization or heuristics, this work proposes a multi–input data–driven dynamic control of uplink power and image compression rate, introducing a Policy Broadcasting Deep Reinforcement Learning approach, based on the Deep Deterministic Policy Gradient algorithm. The proposed solution is aimed at matching the challenging Quality of Service constraints, in terms of maximum round–trip latency and minimum resolution accuracy, while minimizing the energy consumption. Simulations show the effectiveness and scalability of the proposed approach for real–time applications.

In digital transformation, Industrial Data Space (IDS) is a key enabler for industry 4.0 to improve the industrial process, efficiency, and energy consumption by exploiting learning-based techniques. The present paper focuses on improving the decision-making process in complex industrial environments by developing a Deep Reinforcement Learning (DRL) based real-time assistant. Mainly, we address a use case from the space industry to improve the launcher throughput and efficiency and reduce cost by optimally managing the industrial resources. A mathematical formulation of the Industrial Production System (IPS) and a simulated environment are developed to train the DRL-based Proximal Policy Optimization (PPO) agent. The proposed method is scalable and in line with the dynamic nature of the industrial production systems to overcome the domain-dependent heuristics extensively used in the manufacturing industry. Furthermore, simulation results show that the proposed method can provide industrial operators and managers with a real-time decision support system to increase the Return on Assets.

Traffic congestion is among the worst causes of pollution, and the time spent in traffic can cost the world tens of billions of dollars every year. Solutions to mitigate this problem are at hand thanks to the advent of advanced control techniques and artificial intelligence (AI). Traditional traffic light control strategies based on fixed timing of the green, yellow and red phases are simple to implement, but at the same time very inefficient, in particular for busy intersections. This paper discusses both a model predictive control (MPC) approach and a model-free deep reinforcement learning (DRL) algorithm for controlling the traffic lights at a single intersection, with the aim of improving the traffic flow. Firstly, a detailed linear mathematical model of an intersection is formulated and successively tested in a MPC framework; secondly, a DRL algorithm is proposed and verified by comparing it with the currently implemented baseline controller. Finally, the results for the three approaches, MPC, DRL and the baseline controller, are validated through the SUMO (Simulation of Urban Mobility) microscopic traffic simulator.

This paper discusses the problem of assembly line control and introduces an optimal control formulation that can be used to improve the performance of the assembly line, in terms of cycle time minimization, resources' utilization, etc. A deterministic formulation of the problem is introduced, based on mixed-integer linear programming. A simple numerical simulation provides a first proof of the proposed concept.