Reinforcement learning (RL) is a subfield of machine learning which is being developed in Artificial Intelligence (AI). This technique is a data independent process. The primary aim of systems this kind is to maximize their reward signal which makes systems do better things trending to goal. Reinforcement Learning alters with techniques like supervised and unsupervised in such a way that in RL the agent gets up with its own insights and maps what action to perform in certain situations. On the

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... ther hand, Supervised and unsupervised have answers already embedded in them. In RL, in absence of new data, it can learn from its own experience where others can do. RL is used almost everywhere, the best applications of RL in Robotics specifically in motion control, planning it is also used in finance, gaming etc. Here is this paper demonstrating the navigation and motion control development of a 2 wheeled differential drive robot with the help of reinforcement learning topology. Traditionally, to design the behaviour of controllers in robots, we inevitably need models of how the robot actually behaves in the environment. But here we come up with a RL approach to design the control structure for the robot to navigate in the indoor environment.