The Analysis of Experimental Results of Machine Learning Approach

In this article is analyzed a reinforcement learning method, in which is defined a subject of learning. The essence of this method is the selection of activities by a try and fail process and awarding deferred rewards. If an environment is characterized by the Markov property, then step-by-step dynamics will enable forecasting of subsequent conditions and awarding subsequent rewards on the basis of the present known conditions and actions, relatively to the Markov decision making process. The relationship between the present conditions and values and the potential future conditions are defined by the Bellman equation. Also, the article discussed a method of temporal difference learning, mechanism of eligibility traces, as wel as theirs algorithms TD(0) and TD(Lambda). Theoretical analyses were supplemented by the practical studies, with reference to implementation of the Sarsa(Lambda) algorithm, with replacing eligibility traces and the Epsilon greedy policy.