for the eventual reward and so the result of this sparse reward setting is that in reinforcement learning algorithms are typically very sample inefficient which means that you have to give them a ton of training time before they can learn some useful behavior and I've made a previous video to compare the sample efficiency of reinforcement learning algorithms with human learning that goes much deeper into why this is the case and now it turns out that in some extreme cases the sparse reward setting actually fails completely so a