such that in the next episode the reward will be higher and in again in classic reinforcement learning for example in policy gradient methods you will actually calculate a gradient with respect to these weights right here actually let's let's go into that later when we contrast evolutionary methods so the important part right here is that we change the weights in each time step so how do we change the weights of course we don't have access to the reward right in order to change the weights the reward is going to come into play