Unbalanced discounted reward in reinforcement learning : is it a problem?
Most of the RL problems are sequential decision making processes. That is the action taken at time $t$, can influence the future rewards in a sequential way. From this perspective, the first action lead to a sequence of states/actions which produced positive rewards, which means it deserves more positive feedback than the last action. It only seems natural to prefer the first action in the first state unless we know some other action yields better cumulative rewards. Rewards are gains in short-term, an action might be good in the short term but not in the long run, which is why we use cumulative rewards called returns to estimate the value of an action in a given state.