Both reinforcement learning (RL) and the multiarmed bandit (MAB) are well known for modeling the interactions between agents and outside environments in order to achieve the maximum rewards. Interestingly, MAB is often referred to as the one-state RL problem. Could you explain why and compare the differences between these two problems?