Reinforcement learning is concerned with a generic concept of an agent acting in an
environment. From the control theory standpoint, reinforcement learning may be considered as an …

Traction efficiency of farm tractors barely reaches 50% in field operations (Renius et al., 1985).
On the other hand, modern trends in agriculture show growth of the global tractor markets …

Online actor-critic reinforcement learning is concerned with training an agent on-the-fly via
dynamic interaction with the environment. Due to the specifics of the application, it is not …

Control Lyapunov function is a central tool in stabilization. It generalizes an abstract energy
function—a Lyapunov function—to the case of controlled systems. It is a known fact that most …

Reinforcement learning is commonly concerned with problems of maximizing accumulated
rewards in Markov decision processes. Oftentimes, a certain goal state or a subset of the …

Physiological storage disorders affect a range of commercially important pomefruit and result
in fruit losses and wastage of resources. Disorders can develop during and/or after storage …

Heavy-duty vehicles such as tractors, bulldozers, certain construction and municipal
vehicles, soil millers, forestry machinery etc. have a high demand for propulsion force and …

The aim of Reinforcement Learning (RL) in real-world applications is to create systems
capable of making autonomous decisions by learning from their environment through trial and …

Reinforcement learning (RL) in the context of control systems offers wide possibilities of
controller adaptation. Given an infinite-horizon cost function, the so-called critic of RL …

The optimization of vanadium redox flow batteries (VRFBs) is closely related to the flow rate
control: a proper regulation of the electrolyte flow rate reduces losses and prolongs battery …