The retrofitting of heritage buildings represents a significant opportunity to reduce the carbon footprint of our built environment. Within the retrofit process, the desi [...]

Abstrakt: One of the main requests in engineering practice is to have an effective design procedure of a reinforced concrete continuous beam. This task is a multidimensio [...]

The possible method of walking policy obtaining of four-legged robot through Q-learning is discussed in the contribution. Q-learning is implemented using architecture rep [...]

Abstrakt: A great intention is lately focused on Reinforcement Learning (RL) methods. The article is focused on improving model free RL method known as Q-Iearning algorit [...]

Q-learning method proved to be usable in active magnetic bearing (AMB) control task, however the learning speed remains the main problem. Two-phase variant of the Q-learn [...]

This contribution deals with computational modelling of static and dynamic analysis of journal bearings. In this contribution a new theoretical approach to the modelling [...]

Modifications of reinforcement learning algorithm, so called continuous action reinforcement learning automaton (CARLA), are presented in this contribution. Automaton lea [...]

Abstrakt:

The pendulum damper modelled as a two degree of freedom strongly non-linear auto-parametric system is investigated using an approximate differential system. Uni-direction [...]

Asynchronous electric motor control task can be successfully solved using reinforcement learning based method called Q-learning. The main problem to solve is the converge [...]

Locally Weighted Learning (LWR) is a class of approximations, based on a local model. In this paper we demonstrate using LWR together with Q-learning for control tasks. Q [...]

Q-learning is the most popular and effective version of Reinforcement Learning algorithms. In this paper we discuss the possibility of control of a nonstationary system b [...]