Power distribution using adaptive reinforcement learning technique

There are many dynamic situations in which sequence of action come with circumstances favorable. These consequences of action are taken and it shall be concern with the strategies for selecting actions on the basis of both short term and long term consequences. Reinforcement learning is learning paradigm concerned with learning to control a system to maximize a numerical performance measure that expresses long term objective. Main difference between reinforcement learning and supervised learning is only partial feedback that is given to the learner about learner´s prediction. There is great practical importance of adaptive method and this adaptive method can make improvement in decision policy sufficiently, rapidly or may be less. It proposes method for estimating optimal policy in absence of complete model of decision task which are known as adaptive or decision model.