we propose to address this issue through a divide and conquer approach we argue that complex decision problems can be naturally decomposed into multiple tasks that unfold in sequence or in parallel by associating each task with a reward function this problem decomposition can be seamlessly accommodated within the standard reinforcement learning formalism okay so what are they saying right here they are basically saying that if you have a task let's say you want to get whoopsie from here to here and that's very complicated