The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …

Wind-farm flow control stands at the forefront of grand challenges in wind-energy science.
The central issue is that current algorithms are based on simplified models and, thus, fall …

In architectural construction, automated robotic assembly is challenging due to occurring
tolerances, small series production and complex contact situations, especially in assembly …

The use of robots in society could be expanded by using reinforcement learning (RL) to
allow robots to learn and adapt to new situations online. RL is a paradigm for learning …

Action delays degrade the performance of reinforcement learning in many real-world
systems. This paper proposes a formal definition of delay-aware Markov Decision Process …

Action and observation delays commonly occur in many Reinforcement Learning
applications, such as remote control scenarios. We study the anatomy of randomly delayed …

The problem of reducing processing time of large deep learning models is a fundamental
challenge in many real-world applications. Early exit methods strive towards this goal by …

The standard assumption in reinforcement learning (RL) is that agents observe feedback for
their actions immediately. However, in practice feedback is often observed in delay. This …

Many practical applications of reinforcement learning require agents to learn from sparse
and delayed rewards. It challenges the ability of agents to attribute their actions to future …

Several real-world scenarios, such as remote control and sensing, are comprised of action
and observation delays. The presence of delays degrades the performance of reinforcement …