The objective of goal localizationHELM based reinforcement learning for goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions are processed by the agent to reduce the computational effort and increase the speed of convergence. In this paper, reinforcement learning method was utilized to find optimal series of actions to localize the goal region. The visual data, a set of images, is high dimensional unstructured data and needs to be represented efficiently to get a robust detector. Hierarchical Extreme Learning Machine (H-ELM) algorithm was used to find good features for effective representation. The results were analysed by using Matlab progra
