In recent years, QUIC protocol has emerged as a promising alternative to traditional transport protocols like TCP and UDP, offering significant performance improvements in latency and throughput. Additionally, QUIC provides strong security protection ...

In this article, we propose a novel hierarchical framework to resolve within-visual-range (WVR) air-to-air combat under complex nonlinear 6 degrees-of-freedom (6-DOF) dynamics of the aircraft and missile. The decision process is constructed with ...

Faced with a large amount of behavioral data generated in daily activities, the limitations of traditional analytical methods often fail to accurately capture and predict individual behavioral patterns, which not only affects the understanding of ...

The dynamic job-shop scheduling problem (DJSP) is a type of scheduling tasks where rescheduling is performed when encountering the uncertainties such as the uncertain operation processing time. However, the current deep reinforcement learning (...

In this paper, we focus on trajectory optimization for multiple three-dimensional unmanned aerial vehicles (UAVs) serving as aerial base stations (BS) to provide wireless coverage for Internet of Things (IoT) devices. These IoT devices are ...

• Fabrication of variable stiffness joint via 4D printing.
• Simulation and ...

Nowadays, a wide range of robots are used in various fields, from car factories to assistant soft robots. In all these applications, effective control of the robot is vital to perform the tasks assigned to them. Soft robots and ...

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Goals-based wealth management (GBWM), an investment philosophy, is aiming to attain the desired goal or goals specified by an investor, in a long-term investment. A good algorithm for GBWM is able to provide decision-making and enhance the ...

• A new hybrid reinforcement learning for GBWH with extensive practical features.
• New separate neural networks embedding in hybrid policy achieve higher utility.
• Student’s-T distribution in policy sampling beats other distributions.

In the Internet of Vehicles (IoV), multiple applications and multimedia services are deployed to enhance the quality of traveling experienced by passengers. These software components ingest heterogeneous multimedia content (e.g., streams, safety ...

The dynamic job-shop scheduling problem (DJSP) is a typical of scheduling tasks where rescheduling is performed when encountering unexpected events such as random job arrivals and rush order. However, the current rescheduling ...

• The dynamic job-shop scheduling problem with insertions of new orders is studied.

As science and technology have developed, an increasing amount of research on humanoid robots has been conducted. In this paper, a method based on deep reinforcement learning, optimization algorithms, and fuzzy logic for self-guided ...

This paper proposes the design of a quadcopter neural controller based on Reinforcement Learning (RL) for controlling the complete maneuvers of landing and take-off, even in variable windy conditions. To facilitate RL training, a wind model is ...

• Design of a wind model for Reinforcement Learning (RL) training.
• RL-based neural controller for landing & take-off in variable windy conditions.
• Novel neural network controller architecture: addition of an adaptation layer.
• ...

• Literature review of systems maintenance through Reinforcement Learning.
• ...

Nowadays, modern engineering systems require sophisticated maintenance strategies to ensure their correct performance. Maintenance has become one of the most important tasks of the systems lifecycle. This paper presents a literature ...

With the pervasiveness of dynamic task allocation in sharing economy applications, the online bipartite graph matching has attracted people’s increasing attention to its research in recent years. Among its application in sharing ...

Availability of realistic driver models, also able to represent various driving styles, is key to add traffic in serious games on automotive driving. We propose a new architecture for behavioural planning of vehicles, that decide their motion ...

In recent years, deep reinforcement learning (RL) has shown excellent performance in robot control, video games, and multi-agent systems. However, most of existing RL models do not generalize. Even a small visual change will greatly ...

Unmanned aerial vehicles (UAVs) arouse considerable interest in coverage applications such as emergency communication. This paper attempts to address the multi-UAV navigation problem for fair coverage in wireless networks, where a charging station is ...

Mobile edge computing, as a formidable paradigm, sinks the computing and communication resources from the centralized cloud to the edge of networks near to users, which meets the growing demands of mobile applications. However, owing to the ...

Game agents are efficiently implemented through different AI techniques, such as neural network, reinforcement learning, and evolutionary search. Although there are many works for each approach, we present a critical analysis and comparison ...

In this paper, we address a problem of vision-based 3D robotic navigation using deep reinforcement learning for an Autonomous Underwater Vehicle (AUV). Our research offers conclusions from the experimental study based on one of the ...

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• Examining vision-based, deep reinforcement learning approach for Autonomous Underwater Vehicle (AUV) navigation.

This paper shows that the CMAES direct policy search method fares significantly better than PPO gradient policy search for a reinforcement learning task where significant events are rare.