Optimizing Large Search Space using DE Based Q-learning Algorithm

Abstract

Finding global optimum solution in minimum time from large search space is challenging due to involvement of large no. of variables and their varied degree of participation in problem solving process. Complexity of a problem increases with the dimensionality, which must be learnt efficiently to improve performance of the method. Q-learning, a reinforcement learning algorithm is used widely to learn the environment dynamically. However, the conventional Q-learning is not fast and becomes inefficient while solving large scale problem. In the proposed approach by hybridizing Differential Evolution (DE) algorithm and Q-learning (QL) method (QL-DE) optimal partitioning of the search space is obtained involving multiple agents with an objective to achieve maximum classification accuracy. Performance of the proposed algorithm has been compared with state of the art optimization algorithms.