The concept that neural information is encoded in the firing rate of neurons has been the
dominant paradigm in neurobiology for many years. This paradigm has also been adopted …

In this paper, a practical machine learning-based fault diagnosis method is proposed for
induction motors using experimental data. Various single-and multi-electrical and/or …

In this paper, we propose a hierarchical reinforcement learning architecture that realizes
practical learning speed in real hardware control tasks. In order to enable learning in a …

This paper describes several ensemble methods that combine multiple different
reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning …

A key element in the solution of reinforcement learning problems is the value function. The
purpose of this function is to measure the long-term utility or value of any given state. The …

HQ-learning is a hierarchical extension of Q (λ)-learning designed to solve certain types of
partially observable Markov decision problems (POMDPs). HQ automatically decomposes …

Most manipulation tasks can be decomposed into a sequence of phases, where the robot's
actions have different effects in each phase. The robot can perform actions to transition …

In this article, an effective machine learning-based fault diagnosis method is developed for
induction motors driven by variable frequency drives (VFDs). Two identical 0.25 HP …

This paper presents a Q-learning method that works in continuous domains. Other
characteristics of our approach are the use of an incremental topology preserving map …

Suppose we want to use an intelligent agent (computer program or robot) for performing
tasks for us, but we cannot or do not want to specify the precise task-operations. Eg we may …