Improvement of Automated Learning Methods based on Linear Learning Algorithms

Abstract

In recent years, the process of learning creatures is converted to one of the new research area. These researches are divided into two general categories that one of them is based on proposing a solution and learning based methodology to any machines. Learning is defined as changes made in the performance of a system based on experiences. The most prominent features of learning-based systems are that they improve themselves over time. Therefore, learning based machines have a big role in these systems. However, they are not very productive in some application and research areas such as smart real time systems especially. In this paper is proposed a new approach based on reinforcement learning technique that has three versions in order to implementation in different areas. It behaviors based on reward and penalty model. The effectiveness of these interactions with the environment is evaluated by the maximum (minimum) of the number of rewards (penalty) taken from the environment. The main advantage of the reinforcement learning over other learning methods is the need for no information from the environment (except amplification signal). The other learning methods as supervised or unsupervised are not appropriate to these problems. In this method, each agent decides the next its actions based on current k-actions instead of one action. The three versions are simple, sequential and unstructured linear learning methods so they evaluated in different possibilities to get the appropriate responses. Depending on the needs of any system, they can be used. The mode of convergence of actions in the proposed automaton (machine) in six different scenarios is examined.