S. Mojtaba Matinkhah - Computer Engineering Department, Yazd University, Yazd, Iran
Wasswa Shafik - Computer Engineering Department, Yazd University, Yazd, Iran
Mohammad Ghasemzade - Computer Engineering Department, Yazd University, Yazd, Iran

Reinforcement learning (RL) is a promising research area that focusses on the devices that can be interconnected on the internet commonly known as the internet of things (IoT) where it addresses a broad task through making decisions of the device and activity at hand. RL enables interaction of devices and with the environment through probabilistic approach using the response from its own actions and experiences. RL permits the machine and software agent to attain its behavior constructed on feedback from the environment. The IoTs extends to devices to the internet like smart electronic devices that can network and interconnect with others over through connectivity of remote resource being supervised and meticulous. In this paper, we analyzed the main four RL techniques including Markov Decision Process (MDP), Learning Automata (LA), artificial neural network (ANN), Q-learning in relation to its applicability in IoT and state of art solutions to address the challenges. This review provides summarized state of the act analysis on RL techniques that researchers can use to identify current bottlenecks in IoT and suggest models that are in line with dynamics of technologies.