This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward net...

In recent years many research on composite beams with innovative shear transmission with composite dowels have been conducted and innovative composite constructions have been built. One of the common types of shear connection is the method of rolled girders encased in a concrete slab. The combination of such method and pcb (precast composite beam) technology is called pcb-W (precast composite b...

Reinforcement is a critically important process in speciation. It has been credited with often completing speciation, and it is unique in that natural selection directly favors the evolution of reproductive isolating barriers. Reinforcement occurs when selection favors increased prezygotic isolation between two lineages in order to avoid maladaptive hybridization in areas of sympatry (Noor 1999...

This article describes the development of reinforcement learning within the Sigma graphical cognitive architecture. Reinforcement learning has been deconstructed in terms of the interactions among more basic mechanisms and knowledge in Sigma, making it a derived capability rather than a de novo mechanism. Basic reinforcement learning – both model-based and model-free – are demonstrated, along w...

The current evaluation compared the effects of 2 differential reinforcement arrangements and a nondifferential reinforcement arrangement on the acquisition of tacts for 3 children with autism. Participants learned in all reinforcement-based conditions, and we discuss areas for future research in light of these findings and potential limitations.

The distribution of behavior between concurrently available schedules of reinforcement approximates the distribution of reinforcements between the schedules. This equality, called matching, has been explained as an instance of the principle that organisms maximize reinforcement rate. However, a precise account of the relationship between the distribution of behavior and reinforcement rate on th...

This article characterizes the evolutionary algorithm approach to reinforcement learning in relation to the more standard, temporal diierence methods. We describe several research issues in reinforcement learning and discuss similarities and diierences in how they are addressed by the two methods. A short survey of evolutionary reinforcement learning systems and their successful applications is...

Reinforcement learning is an approach for learning optimal action policy via experiencing, i.e. using observed reward in environment states. Reinforcement learning algorithms include adaptive dynamic programming, temporal difference learning and Q-learning[1]. Examples of successful applications of reinforcement learning are controller for sustained inverted flight on an autonomous helicopter [...

This paper compares direct reinforcement learning (no explicit model) and model-based reinforcement learning on a simple task: pendulum swing up. We nd that in this task model-based approaches support reinforcement learning from smaller amounts of training data and eecient handling of changing goals.

Although the use of differential reinforcement has been recommended in previous investigations and in early intervention curriculum manuals, few studies have evaluated the best method for providing differential reinforcement to maximize independent responding. This paper reviews previous research on the effectiveness of differential reinforcement as treatment and describes important areas of fu...