Deduction to supervised learning problem In tabular method, each Q(s, a) could be seen as a parameter. There are more parameters than states, because there are as many parameters for each state as the possible number of actions for each state. There are also situations where states include continuous components. It means we need a … Continue reading Supervised Learning in Reinforcement Learning