Nothing prevents you from rewarding 0 for anything other than a win. A win would be +1 and a loss would be -1 and every intermediate step is 0. However, this yi

Nothing prevents you from rewarding 0 for anything other than a win. A win would be +1 and a loss would be -1 and every intermediate step is 0. However, this yields the new problem of your reinforcement learner needs to explore all paths to all possible outcome - the exact thing we are trying to get around. Thus, we can use human heuristics or neural networks to try and learn these heuristics. The latter approach is deep reinforcement learning and the basis for Alpha Zero.