In this work, an adaptive critic-based neuro-fuzzy is presented for an unmanned bicycle. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the back propagation of error algorithm to tune online conclusion parts of the fuzz...

This paper discusses convergence issues when training adaptive critic designs (ACD) to control dynamic systems expressed as Markov sequences. We critically review two published convergence results of critic-based training and propose to shift emphasis towards more practically valuable convergence proofs. We show a possible way to prove convergence of ACD training.

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The use of these machines in factory automation can improve productivity, increase product quality and improve competitiveness. This paper presents a discussion of recent and future technical and economic trends. During the past twenty years the use of industrial robots that are equipped not only wit...

The SR-based critic learns an estimate of the value function, using the SR as its feature representation. Unlike standard actor-critic methods, the critic does not use reward-based temporal difference errors to update its value estimate; instead, it relies on the fact that the value function is given by V (s) = ∑ s′ M(s, s ′)R(s′), where M is the successor representation andR is the expected re...

A variety of methods for developing quasi-optimal intelligent control systems using reinforcement learning techniques based on adaptive critics have appeared in recent years. This paper reviews the family of approximate dynamic programming techniques based on adaptive critic methods and introduces a new hybrid critic training method.