This paper presents a general fuzzy reinforcement learning (FRL) method for biped dynamic
balance control. Based on a neuro-fuzzy network architecture, different kinds of expert …

Knowledge acquisition is a long-standing problem in fuzzy-rule-based systems. In spite of
the existence of several approaches, much effort is still required to increase the efficiency of …

In this paper we present our novel Fuzzy–Genetic techniques for the online learning and
adaptation of an intelligent robotic navigator system. Such a system could be used by …

Potential field is a reactive method that has been used for trajectory control of mobile robots.
In this method the robot behaves like a particle moving under the influence of an artificial …

O desenvolvimento de arquiteturas de controle para múltiplos robôs em ambientes
dinâmicos tem sido tema de pesquisas na área de robótica. A complexidade deste tema …

Utilizing Uncrewed Aerial Vehicles (UAVs) offers a cost-effective and flexible option for
various applications. However, achieving collision-free autonomous navigation requires …

Aprendizado por Reforço pode ser visto como uma forma de programar agentes utilizando
recompensas e punições para resolver tarefas específicas através de interações com o …

In this paper we propose a new fuzzy-based navigation system for two intelligent mobile
robots using distributed value function reinforcement learning. The robots use their sensors …

Este trabalho aborda o problema de planejamento de caminhos em robótica móvel
autônoma utilizando campos potenciais. Dentre as várias técnicas de campos potenciais …

Reinforcement learning agents store their knowledge such as state-action value in look-up
tables. However, loop-up table requires large memory space when number of states …