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This category of algorithm has no predefined data and the input depends upon the action taken by the agent and these actions are then recorded in the form of matrices so that it can serve as the memory to the agent. The agent explores the environment and collects data which is further used to get the output. In medicine, there are several instances of reinforcement learning (RL) application like for the development of therapy plan for lung cancer [18] and epilepsy [19]. Deep RL approach has been recently proposed for the therapy plan development on medical registry data [20] and also to learn treatment strategies for sepsis [21].