Synchronous Q Learning Based Technique for Performance Improvement in Multi core Processors

Conventional processors are widely used in many practical applications such as weather forecasting, AI, Ocean modeling, Big data analysis, etc. In this research work we have investigated various parallel computing approaches to improve the performance in terms of execution time. It is shown by simulation that multi processor systems takes less time. We have literature two algorithms namely Classical Q-learning and Modified Q-learning i.e. Synchronous Q-learning are available for load balancing from this point of view we have developed a new approach of dynamic load balancing technique. In the present work we have combine two algorithms and developed a new algorithms under the name Synchronous Q-learning Algorithms. It is shown by simulation that proposed Synchronous Q-learning algorithms takes less time. In this paper, we exhibit new Synchronous Q learning algorithm that consolidate components of policy iteration and classical Q learning/esteem iteration to effectively learn and control arrangements for a dynamic load adjusting situations utilizing reinforcement learning techniques.