Type 1 diabetes is a growing global health challenge. Standard clinical practice often relies on manual insulin injections, which can lead to suboptimal glucose regulation. Recent advancements have shifted focus towards Artificial Pancreas systems, integrating continuous glucose monitoring with automated insulin delivery. This work presents a quantitative comparison of four Deep Reinforcement Learning algorithms for autonomous glycemic regulation via insulin injection: DDPG, PPO, SAC, and TD3. The validation is conducted using the Hovorka model, in presence of uncertainties on number, time and amount of meals. Results show that all four controllers are able to maintain blood glucose levels within the target range. The TD3 algorithm outperforms the others in terms of several key performance indicators such as time in range, time in hypo/hyperglycemia and total insulin usage, while also exhibiting fewer hyperglycemic episodes compared to prior works in academic literature.

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.

Artificial Intelligence and Machine Learning have brought transformative changes to clinical diagnostics, especially in image classification via deep convolutional neural networks. The latter are crucial in analyzing and identifying diseases from medical visuals, ensuring accurate diagnosis and prevention. However, the limited availability of medical imaging data poses a serious challenge and leads to poor performance of the classifier. To address this issue, both geometric/color augmentation and synthetic data generation (through Generative Adversarial Networks and Variational Autoencoders) methods are employed, both operating on the entire dataset without an intrinsic optimization of the classification process. This study introduces an automated data augmentation strategy in which the editing operation is customized with respect to the individual images contained in the training set. This is done by using the Deep Reinforcement Learning framework provided by the Proximal Policy Optimization algorithm, with the reward being the test accuracy of the image classifier. The application of the proposed procedure on a meager dataset related to gastrointestinal diseases demonstrates an improvement in image classification by over 3%.