Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization.

In this paper we present a cost-effective approach of building a hardware microcontroller for simulating the M1A2 Abrams battle tank. We have focused on modeling the battle tank gunner station controls, as well as a virtual representation of the tank itself. We have additionally simulated the flight paths of the four main cannon rounds. Finally, we have conducted several experiments on firing at targets, compared the results with actual data sheets and draw conclusions.

Modern warfare is inextricably linked to the use of UAVs. They possess some weak points that make them vulnerable to the influence of the adversary. The paper proposes a concept for an AI-controlled UAV system that overcomes the shortcomings of autonomous UAVs while not significantly increasing the cost of an individual UAV. The UAV system also implements many additional functionalities.

In this paper we present a novel approach for building a common software platform for simulating armored combat vehicles. We use Unreal Engine 4 as our simulation software. The presented approach is an attempt towards integrating different combat vehicle modules into a simulated environment. The presented simulator architecture can be used in various training combat scenarios, such as reconnaissance, coordinated firing on targets, different cooperation scenarios, etc.

The commercial driver profession – operators of trucks, busses and other specialized vehicles is a hazardous one. People in these positions are required to The commercial driver profession – operators of trucks, busses and other specialized vehicles is a hazardous one. People in these positions are required to

Monitoring psychophysiological signals in training and real-world scenarios is crucial for optimal personnel performance, especially in high demanding jobs - military pilots, commercial truck drivers, heavy lifters, tunnel - boring machines, etc. In this paper, we propose a prototype monitoring system that measures vital signs, estimates stress levels and attention to the given tasks. The system is intended for military pilots' annual evaluation, as well as initial aviation training. Nevertheless, the current study suggests that such a system can easily be integrated into other high-risk domains and professions. For the system implementation, we use commercial-off-the-shelf (COTS) connected devices and software, such as VR headset with integrated eye tracking (Varjo VR-3), budget USB polygraph and flight simulation software (Digital combat simulator). We gather data via those sensors, measure the pilots' performance under stressful conditions and draw conclusions.